Proactive Maintenance and Data-Driven Optimization of Mineral Lubricating Oil in a Gas Engine Cogeneration System Extending Oil Change Intervals for Cost Savings and a Reduced Environmental Footprint

Abstract

This study investigates the operational properties of mineral lubricating oil in gas engines used in cogeneration systems, with a focus on factors contributing to the degradation of lubricating properties critical for energy efficiency and system management. The research was conducted on a 4.3 MW gas engine operating for about 90,000 machine hours, using natural gas as fuel. Data obtained from SCADA (Supervisory Control and Data Acquisition) systems and laboratory analysis were utilized to establish oil quality criteria, enabling the prediction of oil degradation and optimization of oil change intervals. Parameters including viscosity, contamination levels, Total Base Number (TBN), and Total Acid Number (TAN), were identified as significant indicators of oil performance and engine reliability. The findings revealed that oil change intervals could be extended by an average of 37% compared to standard schedules, thereby minimizing unnecessary maintenance downtimes, enhancing system availability, and increasing electrical and thermal energy output. Optimized oil utilization reduced material costs for oil and filter replacements, lowering expenditures from 3021 to 1887 EUR per machine hour. Additionally, the predicted Global Warming Potential (GWP) for prematurely consumed oil amounted to 68 × 10³ kg CO₂ eq., while avoidable waste generation reached 18.2 m³ of mineral oil. Regular oil analysis conducted every 1000 operating hours proved critical for early detection of oil degradation, supporting proactive maintenance strategies and ensuring optimal engine performance and longevity.

1. Introduction

Energy has always been central to the advancement of civilization and economic development. While traditional energy systems have relied on fossil fuels, growing environmental concerns, such as those regarding CO₂ emissions and climate change, have accelerated the transition to cleaner and more efficient solutions [1,2]. One of the main challenges in modern energy systems is ensuring reliability and stability, particularly with the increased use of renewable energy sources like wind and solar, which are inherently intermittent [3,4]. In this context, cogeneration has emerged as an effective and reliable solution. By enabling the simultaneous production of electricity and heat, cogeneration systems—especially those based on gas engines—significantly improve fuel efficiency and reduce greenhouse gas emissions. These systems address the modern need for sustainable energy generation while ensuring operational flexibility and environmental benefits [5,6].

Cogeneration, dating back to the late nineteenth century in Europe and the early twentieth century in the United States, allowed industrial plants to generate electricity to meet their own energy needs. The oil crisis of the 1970s heightened interest in cogeneration, and modern, efficient distributed generation models have revitalized its appeal. The potential of cogeneration for cost savings and infrastructure reduction makes it particularly valuable in the context of current energy requirements [7]. Polygeneration develops the concept of cogeneration by producing multiple energy carriers, such as electricity, heat, cooling, and steam, within a single installation. The key components of polygeneration systems include cogeneration units, absorption chillers, and steam generators, allowing efficient and multifunctional use of energy sources. Integrating these systems into electrical, thermal, and gas networks improves reliability while maximizing energy efficiency, offering both economic and environmental benefits [8].

In gas-powered engines within cogeneration systems, the use of the appropriate lubricant oil is a priority. Modern engines must be lubricated with the correct oils to ensure reliability, durability, and long-term operation and reduce the risk of failure [9]. Proper oil selection, according to the engine manufacturer’s recommendations, is essential to achieve the required engine performance and ensure reasonable maintenance costs. It helps avoid increased maintenance expenses and reduces the frequency of service repairs. Lubricants can vary significantly even within the same viscosity grade. Issues related to changes in fuel composition and lubrication in gas engines require precise monitoring to ensure their efficiency and long life. Mineral and synthetic oils differ in the properties that affect their operation, especially under challenging conditions. Synthetic oils generally outperform mineral oils in particularly harsh operating conditions, reducing maintenance costs and increasing efficiency [10].

The diagnostics of internal combustion engines play a crucial role in ensuring their efficiency and reliability. Burdzik et al. [11] emphasize the importance of information on engine performance parameters and operating conditions, which allows the targeting of preliminary diagnostics and also influences the failure rate and repeatability of faults. Figlus et al. [12] present an innovative approach to diagnosing the fuel system of internal combustion engines, based on the vibroacoustic analysis of the components within this system. This approach allows for identifying the source of disturbances in engine operation and accelerating the diagnostic process. Figlus et al. [13] describe measuring vibrations in the fuel system of an engine with a faulty injector, enabling effective fault diagnosis. The findings of Figlus and Cupiał et al. [14,15] are particularly significant, as they analyze the operating periods of gas engines, including availability, number of starts, runtime, and downtime, offering valuable insights into their long-term operational efficiency.

Mineral oil serves as a primary lubricant in gas engines due to its physicochemical properties and ability to provide effective lubrication under demanding operating conditions. Thermal stability, resistance to oxidation, and lubricating performance are key factors determining its application [16,17]. As a product of crude oil refining, mineral oil exhibits characteristics influenced by the quality of the base oil and its purity. The refining process, along with the selection of enhancing additives such as antioxidants, significantly improves oxidation resistance and lubricating capabilities [16,18,19]. Research indicates that the quality of base oil and the use of antioxidant and anti-wear additives are crucial for protecting critical engine components, such as pistons, rings, and bearings [20]. Modern refining technologies enable the production of oils with superior thermal stability and resistance to degradation [16,17], while market analyses confirm that high-quality mineral oils can provide protection comparable to synthetic oils at significantly lower production costs [21]. Gas engines, which operate at high temperatures and require extended service intervals, demand oils with stable thermal properties. Oxidation processes in oil lead to the formation of deposits, increased acidity, and higher viscosity, which negatively affect lubrication efficiency and engine performance [19,22,23]. To ensure engine reliability under challenging operating conditions, mineral oils enriched with antioxidants and detergents are essential, as they extend oil durability and replacement intervals [24,25,26]. Lubricating properties such as viscosity and the ability to form a stable oil film play a key role in reducing mechanical wear. Studies confirm that high-quality mineral oils effectively reduce friction and wear, directly contributing to extended engine life [22,23,27]. Regular monitoring of oil parameters allows for accurate prediction of oil degradation and optimization of replacement intervals, directly improving cost-efficiency [18,20,28]. A comparison of mineral and synthetic oils highlights significant differences in their performance. With proper quality control and scheduled replacements, mineral oils remain a cost-effective alternative for gas engines [23,29]. Optimizing their formulation with anti-wear and detergent additives ensures their effective use under high-load conditions [19,22,23]. From an environmental perspective, the use of mineral oils generates waste that requires proper disposal. Reducing emissions of pollutants such as sulfur and combustion by-products is achievable through the use of high-quality oils and proper operational practices [16,30,31]. LCA analyses show that optimizing mineral oil replacement intervals significantly reduces its environmental impact while enhancing the efficiency of gas engines [16,19,25]. Parameters such as viscosity, TBN, and oxidative stability are critical for evaluating the quality of mineral oils, which play a pivotal role in maintaining engine efficiency and reliability. Viscosity determines the effectiveness of lubrication and wear reduction, while the TBN, which measures oil alkalinity, is an essential parameter for neutralizing acids formed during combustion. A decline in TBN during operation indicates ongoing oil degradation, which can lead to engine corrosion and deposit accumulation [32]. Regular monitoring of these parameters is vital for sustaining optimal engine performance and extending its lifespan, particularly in industrial and automotive applications [33]. Regarding oil replacement, a drop in TBN serves as a primary indicator for optimizing service intervals. The literature shows that oil degradation depends on operating conditions and oil composition, with no universally accepted replacement threshold. Proposed limits range from 2 to 5 mg KOH/g, while some sources recommend replacement when the TBN decreases to 50% or 30% of its initial value [34,35,36]. Studies confirm a rapid decline in TBN [37,38], and predictive models for TBN changes can serve as components of oil replacement optimization systems [39,40,41].

The improvement of engine designs and the enhancement of their ecological and economic efficiency are priorities in the development of modern technologies. The introduction of numerous innovations in engine construction, material selection, and internal loss reduction not only leads to higher energy efficiency, but also contributes to the reduction in harmful emissions into the atmosphere, aligning with current environmental standards. In response to growing ecological demands, solutions that maximize energy utilization are needed [42]. In addition to innovations in engine design, it is also crucial to utilize a portion of the energy released during the combustion process in internal combustion engines, which helps reduce the negative environmental impact. All of these efforts reflect a modern approach that focuses on increased energy efficiency and minimizing the carbon footprint. Cogeneration units, which use gas as fuel, align perfectly with the trend to maximize energy production while minimizing emissions of pollutants released from installations into the environment [43]. Therefore, an effort was made to assess the impact of using information on the composition of lubricating oil on the operation of gas engines in cogeneration systems. A method was developed for the early diagnosis of engine malfunctions by analyzing changes in operating parameters based on the analysis of the lubricating oil. Since engine oil degradation processes during operation lead to changes in its performance properties, the aim of the study was to verify the feasibility of using oil-based criteria to predict oil degradation under industrial conditions. This approach aimed to establish the optimal time for oil replacement in cogeneration units, where gas is used as fuel and the engine moving parts are lubricated with mineral oil [22,44].

Determining the impact of changes in the properties of lubricating oils on operation allows a justified extension of the reliable operation of cogeneration systems. It contributes to reducing friction in the engine, leading to lower fuel consumption and a reduction in CO₂ emissions, which positively impacts both the environmental aspects and the operational efficiency of the cogeneration installation. The estimated properties of lubricating oils allow for a decrease in oil change frequency, thus minimizing the amount of waste oil. From an operational efficiency perspective, understanding the relationships among lubricating oils allows for the precise determination of oil change intervals, which, in most cases, extends the oil’s service life, consequently reducing operational costs and machine downtime [45]. Consequently, evaluating the properties of lubricating oil reduces the risk of engine failure and extends its service life, while also improving fuel efficiency, leading to savings in fuel consumption. These factors emphasize the crucial role of lubricating oils in improving both operational efficiency and environmental impact. It has been demonstrated that oil quality criteria can be effectively used to predict engine oil performance in gas-powered cogeneration modules, which simultaneously affects the ecological aspect, environmental pollution, and efficiency of energy production processes.

2. Materials and Methods

This research investigates an industrial cogeneration system powered by an internal combustion engine that uses coal mine methane. This system is designed to generate both electrical and thermal energy, with the engine operating on the Miller thermodynamic cycle and connected to a synchronous generator producing electrical power, provided in Figure 1.

The system integrates heat exchangers for exhaust gases (AWT), engine block cooling water (KWT), lubricating oil (SWT), and the district heating network (HWT). High-temperature water (red arrows) serves as the primary carrier of thermal energy, while cooled liquid (blue arrows) ensures efficient heat transfer and cooling. Auxiliary circuits (purple arrows) are responsible for oil circulation and cooling within the C-NK system. The orange exhaust gas circuit highlights an additional element for heat energy recovery within the system.

The waste heat is recovered for district heating. The system is equipped with a dual oil lubrication setup, consisting of both external and internal systems, and is continuously monitored for oil levels. Data from the Asix platform by ASKOM and laboratory tests were used to establish oil quality criteria, allowing the prediction of oil degradation and the adjustment of oil change intervals. Asix integrates functionalities of SCADA (Supervisory Control and Data Acquisition), HMI (Human–Machine Interface), and MES (Manufacturing Execution Systems), providing comprehensive tools for monitoring, controlling, and managing industrial processes. Regular laboratory oil analysis is conducted to assess the oil condition, and oil and filter replacements are performed based on the engine’s operating hours. The system is fully integrated into the national power grid and adheres to environmental emission standards. The primary focus of the study is to evaluate how oil properties influence engine performance and operational efficiency, ensuring optimal engine performance and longevity through routine maintenance and analysis.

2.1. Experimental Platform

The study of changes in engine oil properties and their impact on the operation of internal combustion engines was carried out on real industrial installations. The research setup includes a cogeneration system that uses an internal combustion engine powered by methane gas from the mine degassing. This engine drives an electric generator that produces electricity supplied to the national power grid. The heat generated in the engine cooling system during operation is recovered and directed to the district heating system, either partially or in full. The goal of operating the cogeneration unit is to maximize electricity production, while thermal energy is adjusted to the current demand for heat in the heating system. During the heating season, maximum heat recovery is ensured, while in other periods, only the necessary heat is supplied to meet the needs of consumers.

The heat recovery system facilitates the generation of heat at temperatures up to 124 °C. The engine, fueled by mine gas and operating with a lean fuel mixture, is a 16-cylinder unit with a V-shaped configuration, featuring two turbochargers (one for each cylinder bank) and two intercoolers. The engine runs on the Miller thermodynamic cycle, a cycle patented by Ralph Miller in 1957 [46]. Engine operation is similar to the Otto cycle but features a longer intake valve during the intake stroke, which is divided into two phases: opening and closing of the valve. The engine is started using an air-start system powered by compressed air. After synchronization with the power grid, the engine operates at a constant speed of 1000 rpm, regardless of the load. Due to its design characteristics, the engine can continuously operate within a load range of 65% to 100%, while for a limited time (up to 4 h), the load can range from 50% to 65%. The calculated gas demand at a 100% CH₄ content is approximately 910 Nm³/h; at a 60% CH₄ content, it is approximately 1520 Nm³/h; and at a 40% CH₄ content, it is approximately 2300 Nm³/h. The lubrication system includes an oil pump mounted on the engine shaft. Before starting the engine, both the oil and the engine block are preheated and the initial lubrication is performed using an electric oil pump. The lubrication system consists of an oil sump located beneath the engine block, with an integrated glass oil sight gauge and a remote reading system, allowing level monitoring through the Supervisory Control and Data Acquisition (SCADA) system. The oil system is connected to external fresh and used oil tanks, integrated with the oil catch basins. The engine is equipped with an automatic oil level replenishment system for the oil sump, and an electric pump is installed on the fresh oil tank, which, when necessary, pumps oil into the sump, allowing this operation to be performed while the engine is running, thus eliminating the need to shut down the engine. To monitor the oil condition, a meter is installed in the system and its readings are periodically recorded in operational reports. The pump system, along with the fresh and used oil tanks, can also be used for oil changes, although this operation can only be performed during engine downtime, after the appropriate shut-off valves have been set [47].

The internal combustion engine is directly coupled to a three-phase synchronous salient pole generator, which is integrated with the national power grid. This generator operates at a terminal voltage of 6300 V and a frequency of 50 Hz. The engine delivers an electrical output of 4.0 MWe, powered by 9.3 MWt of fuel input. The engines operate within a load range of 65% to 100% at a constant rotational speed, with thermal conditions remaining stable and continuously monitored by the engine control system. The control system is configured to prioritize maintaining safe and optimal operating conditions. If any parameters deviate from acceptable limits, the system automatically signals the need to reduce engine power. This reduction adjusts the power output to a level that ensures safe operating conditions, effectively preventing potential damage to the engine components. The Miller-cycle engine is designed to save approximately several percent of energy, improving energy generation efficiency. The resulting energy gains enhance performance and, most importantly, operational cost-effectiveness, as the system generates electricity for the power grid and heat for the needs of the local community. Table 1 outlines the specific parameters of the engine and the performance characteristics of the generator for this gas-fueled configuration.

The generator utilizes solid grease lubrication, applied to each bearing’s lubricator at scheduled intervals. The engine diagrams with labeled components are provided in Figure 2 and Figure 3.

The cogeneration system is equipped with an automatic ignition timing adjustment system. The ignition timing is controlled in real time by the engine’s central control unit, which continuously monitors the combustion in the cylinders by analyzing the characteristics of the combustion process. This system automatically adjusts the gas mixer settings and ignition timing to dynamically optimize performance.

Studies on the impact of mineral oil properties on the operation of internal combustion engines were conducted in an industrial setup powered by mine gas (methane), which has accumulated nearly 90,000 operating hours. The lubrication system under analysis includes an external and an internal oil circuit. The external oil system consists of the following:

• A fresh oil tank (2000 L) with a drip pan and an oil pump;

• A used oil tank (2000 L);

• An oil refill meter to monitor engine oil top-ups.

• The internal oil system includes the following:

• Oil pumps;

• Electric oil heater;

• Oil filters (main and fine particulate);

• Oil level sensor.

The total capacity of the oil system is approximately 2200 L. Lubrication management involves maintaining the appropriate level of fresh oil, monitoring the amount of oil used, and checking the oil levels on indicators. Oil and filter replacements are performed during engine operating hours, with regular laboratory analysis of the oil.

2.2. Object of the Study

The object of the study is the lubricating oil contained in the cogeneration system. The engine manufacturer’s technical documentation specifies the recommended types of oil for use as lubricants. A total of 63 different oils were approved for use in the system, consisting of 59 mineral oils and 4 synthetic oils, as presented in Table 2.

These oils are classified into groups according to the content of sulfated ash, as shown in Table 3.

Analysis of low- and high-ash oils showed that high-ash oils have a higher Total Base Number (TBN), which is beneficial for the installations analyzed. However, the other parameters, particularly the sulfated ash content, indicate the advantage of using low-sulfated ash oils. Based on this analysis, the use of low-ash oils was recommended, which are characterized by the properties shown in Table 4.

The physicochemical properties of the tested mineral oil are shown in Table 5.

The entire oil system of the engine is shown in Figure 4.

The synoptic panel displaying the lubricating oil system includes visual indicators for oil installation. It provides real-time information on the oil levels, which are monitored through a combination of a glass oil gauge and digital readings. The panel shows the status of the entire lubrication circuit, including the oil tank, pumps, and filters. Additionally, it indicates any necessary maintenance actions, such as oil changes or filter replacements, based on engine operating hours. The oil is contained in the engine oil pan, with its level indicated on a local glass oil gauge (on the front wall of the engine block). In addition, the oil level in the oil pan is digitally displayed on the synoptic panel.

The local oil gauge is equipped with a low-level signalization system. If the oil level falls below the minimum threshold, the oil replenishment pump is activated to transfer the oil from the fresh oil tank to the external oil system. The oil is pumped until the oil level reaches 75% of the oil pan’s capacity. Oil samples are taken regularly for analysis to evaluate various parameters that influence oil quality and condition. The analyzed parameters include the following:

• Lubricating parameters, such as viscosity, pH, acid number, base number, and water content;

• Indicators of oil wear, such as the presence of metals (e.g., silver, aluminum, chromium), which can indicate the wear of specific engine components;

• Contaminants and additives that improve the oil performance;

• Following a detailed analysis, a report is generated to assess the overall condition of the oil;

• The oil is suitable for continued operation;

• Caution is advised and service actions may be required;

• Oil replacement is necessary.

One of the main indicators of oil wear is the relationship between the TBN (Total Base Number) and TAN (Total Acid Number). The TAN, also known as the neutralization number, measures the acidity of the oil. Active substances in the lubricant affect the TAN value, which for new oils can range from 0.5 to 2 mgKOH/g. During the oxidation and nitration processes, weak organic acids are produced, which are partially neutralized by the alkaline properties of the oil. There is a general correlation between an increase in TAN, the aging of the lubricant, and the nitration of the oil. The TAN value should not exceed the TBN value. The TBN represents the alkaline reserve of oil and indicates its chemical ability to neutralize acids. This property is crucial for controlling corrosive wear. During oil operation, the TBN decreases as it reacts with acids formed by the combustion process, oxidation, and nitration of the oil. The TBN value should be at least 2.0 (mg KOH/g), because this value is accepted in industry standards, and is advisable to ensure effective acid neutralization and engine protection. Below this value, the oil may no longer effectively neutralize acids, leading to corrosion and other issues. As oil ages, the TBN decreases while the TAN increases. When the TBN and TAN values equalize, it signals the need for oil replacement due to the decreased protection against corrosion. Effective oil management requires continuous monitoring to ensure optimal engine performance and reduce the risk of failure. By tracking TAN and TBN values, oil change cycles can be optimized, minimizing the risk of engine damage and maximizing engine efficiency [48,49].

2.3. Research Methodology

By analyzing the chemical composition and oil quality parameters, fluid lubricant change intervals were reassessed at significant events, such as the following:

• Engine start-up;

• Operational mode changes;

• Post-maintenance activities.

• The analysis was performed using data obtained from the SCADA system and laboratory oil tests conducted in a specialized laboratory. The focus of the analysis included the following oil quality parameters:

• Total Acid Number (TAN);

• Total Base Number (TBN);

• Oxidation and nitration.

Based on the oil quality parameters, the next complete oil change was predicted. The engine inspections and maintenance activities were carried out as per the technical operational documentation, as presented in Table 6.

Based on oil change intervals and chemical composition analyses, a criterion was developed to predict the optimal timing for the next oil change. Prior to conducting the analysis, the data underwent thorough preparation:

• Periods with too few measurements or short durations were removed;

• Outliers (results deviating significantly) were excluded.

• To ensure clarity in the description of the charts, the following parameter symbols were used:

• EWH—the number of operating hours of the oil since the last replacement [10³ h];

• KV100—kinematic viscosity at 100 °C (mm²/s);

• TAN—Total Acid Number (mg KOH/g);

• TBN—Total Base Number (mg KOH/g).

Given the operational condition that the TAN number should not exceed the TBN number, an oil quality coefficient was defined as follows:

IND = TBN − TAN(1)

Oil replacement should be conducted earlier when

IND < IND_cr(2)

For this analysis, the critical value IND_cr was assumed to be 0.25. The prediction of oil operating time was carried out based on the approximation of the oil quality coefficient between oil changes. A linear approximation function was assumed:

IND = a₀ + a₁EWH(3)

The coefficients a₀ and a₁ were determined using the least squares method to find the best fit for a set of data points. The predicted oil operation time was calculated by solving the following equation:

a₀ + a₁EWH − IND_cr = 0(4)

To assess oil quality, the oil quality coefficient IND was defined, with oil replacement recommended when IND falls below a critical threshold, and a linear approximation model was used to predict the oil operation time based on the relationship between operating hours (EWH) and the oil quality coefficient, with parameters a₀ and a₁ estimated by numerical approximation with the least squares method.

Data analysis and prediction were conducted using the Python programming language, leveraging the SciPy and Seaborn libraries. This methodology ensures robust statistical analysis and effective data visualization, which are crucial for interpreting trends and validating results. To predict the optimal oil change interval based on TBN, the following steps were performed:

• Data collection and preprocessing—historical TBN values were collected during systematic oil property assessments at regular machine operating intervals. The data were cleaned to eliminate anomalies and incomplete records that could distort the analysis.

• Exploratory data analysis (EDA)—using the Seaborn Python library, TBN degradation trends over time were visualized to identify patterns and relationships. Graphs were employed to observe the rate of TBN decline, while heat maps were used to analyze correlations between the TBN and operational parameters.

• Trend modeling and regression analysis—to predict TBN depletion, a regression model was developed using the SciPy Python library. A linear regression approach was applied to model the trend accurately based on the observed data.

• Parameter estimation—the curve_fit function from the SciPy library was used to fit model parameters to the experimental data. This allowed precise quantification of the TBN degradation rate under specific engine operating conditions.

• Oil change interval prediction—based on the fitted model, the time at which the TBN reaches its critical threshold, as defined by operational guidelines, was determined. Solving the regression equation with respect to machine operating hours enabled the prediction of the optimal oil replacement time.

• Validation and error analysis—the accuracy of the model was validated using a test data set. Residual analysis, Root Mean Square Error (RMSE), and the coefficient of determination, R², were calculated to evaluate the predictive performance. A comparison of predicted and actual TBN values confirmed the reliability of the model.

• Result visualization—the results were graphically presented using the Seaborn Python library. Forecasted TBN depletion curves were overlaid with actual measured values, providing a clear visual comparison.

The Global Warming Potential (GWP), expressed in kg CO₂ eq., was estimated based on a literature review. Additionally, using available data, the excess volume of hazardous waste generated from prematurely discarded mineral oil was calculated in cubic meters (m³). The Life Cycle Assessment (LCA) methodology, applied to evaluate environmental burdens across various stages of the product’s life cycle—such as production, transport, usage, and disposal—enabled the identification of GWP as a selected environmental indicator. A literature review on LCA as a comprehensive environmental assessment tool, integrating multiple impact parameters, provided the basis for estimating results. Among the analyzed indicators, GWP was selected for this study. Measurement data allowed for the calculation of unnecessary hazardous waste generation caused by shortened oil service intervals. The estimated LCA results for mineral lubrication oil were obtained by collecting data from publications [41,42,43,44,45,46,47,48], compiling the maximum and minimum values, and calculating the arithmetic mean. Subsequently, the amount of unused oil resulting from unnecessary changes was determined, and the GWP was calculated. This information is intended to assess the environmental impact of the operation.

3. Results

The impact of engine oil properties on engine performance in a cogeneration system was analyzed. Oil quality criteria were established to predict the timing of the next oil change, focusing on engine efficiency and longevity. Oil properties such as viscosity, contamination levels, and TBN and TAN values directly influence engine operation and component wear. By applying the appropriate criteria, continuous monitoring of the oil condition enables optimized oil change intervals and prevents premature wear.

3.1. The Impact of Engine Oil Properties on Engine Performance

The system has a fixed engine oil capacity, with top-ups compensating for oil losses due to evaporation, combustion, leaks, or potential minor leaks. To improve the properties of the engine oil, periodic oil refills were carried out. Figure 5 shows the graph of oil top-ups to the engine, with values relative to the total capacity of the lubrication system indicating that sometimes more than 50% of the oil in the system was new.

Mineral oil top-ups were carried out based on the analysis results. In most cases, the top-ups reflected the oil consumption by the engine. The total oil volume remained close to the values shown on the chart, fluctuating between MIN and MAX indicators. The oil level was monitored by the engine diagnostic system, which automatically triggers the oil replenishment procedure in the engine oil sump when the level drops. The data on oil top-ups indicate that they were not carried out regularly throughout each operational period. The initial 8000-h period stands out with minimal oil top-ups, suggesting low oil consumption and possibly effective sealing and favorable operating conditions. An increase in top-ups between 8000 and 10,000 h may signal the first signs of oil consumption or minor leaks. Significant and frequent top-ups occurred between 24,000 and 40,000 h, with some periods adding more than 1 m³ of oil (1.7 m³ around 24,000, 30,000, and 36,000 h), indicating a substantial increase in oil consumption, likely due to wear or component degradation. Consistent top-ups between 40,000 and 80,000 h represented a clear trend of regular additions, with several points showing 1 to 2 m³ in short intervals. According to the engine manufacturer’s guidelines, engine block replacement with the piston–cylinder system should occur at 64,000 h, but after 75,523 h, the engine had a new block, pistons, and cylinders, with unexplained excessive oil consumption. It is not clear whether this was due to engine failure or poor fitting during assembly. The engine did not experience significant leaks during its operation. The total amount of oil in the system remained consistently at 2.2 m³ throughout the operational period.

The increase in oil top-ups with increasing engine operating hours is caused by several factors. As engine hours accumulate, typical wear on seals and components leads to increased oil consumption. More operating hours often correlate with gradual increases in temperature and load on components, which cause oil degradation and, consequently, higher consumption. The constant top-ups in the later stages of the cogeneration system suggest that oil depletion is occurring at a more regular rate, likely due to leaks that accumulate. The observed patterns suggest that seal replacement between 24,000 and 30,000 h could reduce the frequency of top-ups. Based on the data, after approximately 40,000 h, more intensive maintenance may be necessary, as top-ups became more frequent and larger in volume after exceeding certain thresholds, particularly around 40,000 and 80,000 h. This analysis provides insight into the gradual increase in oil consumption, reflecting wear, and emphasizes the importance of regular monitoring and maintenance to maintain optimal performance.

Indicators obtained from laboratory test results show specific changes as a function of operating hours, reflecting oil aging processes, degradation, and replenishments. A decrease in TBN and an increase in TAN (as shown in Figure 6), along with oxidation and nitration (as shown in Figure 7), suggest growing chemical processes that reduce the oil’s quality.

The figure illustrates changes in TAN and TBN during engine operation, including oil top-ups. Black points representing values below 2.1 m³ correspond to oil top-ups, while higher values indicate full oil changes. Regular top-ups of fresh oil stabilize TBN levels, particularly after sharp declines, and help extend the oil’s service life. The parameter changes shown on the graph over time and volume indicate that while regular top-ups slow the rate of degradation, full oil changes play a dominant role in halting the degradation processes. Synchronizing oil parameter monitoring with maintenance activities has enabled more effective oil quality management and ensured safe operating conditions for the engine. As operating hours increase, the oil loses its protective properties, which require regular analyses and periodic oil changes to maintain the desired lubricating properties. The TAN graph shows a gradual increase, particularly noticeable at the beginning and end of the operating-hour range, with values oscillating in the middle. Up to around 10,000 operating hours, there is a noticeable increase in TAN, suggesting increased acidification of the oil at this stage of use. It exceeds a level of 2, indicating a significant increase in acidity. Between 10,000 and 30,000 operating hours, the TAN value stabilizes, oscillating around 1.5–2, which may indicate a chemical equilibrium in which acidification processes are balanced by oil top-ups. Above 50,000 operating hours, the TAN increases again, suggesting a reduced effectiveness of the acidity stabilizing additives as the oil continues to degrade.

The TBN graph shows a downward trend, indicating the depletion of the oil’s ability to neutralize acids as it is used. Up to around 20,000 operating hours, the decline in TBN is significant, dropping from about 4.8 to below 3.5, likely due to chemical reactions occurring within the oil. Between 20,000 and 40,000 operating hours, the TBN oscillates around a value of 3, with occasional increases, suggesting fluctuations in the acid-neutralizing capacity of the oil, possibly due to oil changes or additive regeneration. Above 50,000 operating hours, the TBN stabilizes around 3.5, indicating that the oil has lost some of its protective properties.

The oxidation graph shows dynamic changes in values with an upward trend, suggesting an increasing degree of oil degradation. Up to around 10,000 operating hours, oxidation values are low, indicating that the oil aging process is limited in the early stages. Between 10,000 and 40,000 operating hours, oxidation increases to a level of 10–12, then fluctuates, with occasional peaks suggesting periods of intense oxidation, which could affect the oil’s properties, such as viscosity. Above 50,000 operating hours, high oxidation values (up to approximately 14–15) indicate advanced oil aging, which may require urgent oil replacement. The rapid oxidation of engine oil shown in Figure 7 resulted from a series of cylinder head failures, which caused coolant system leaks and the infiltration of cooling fluid into the oil. Consequently, the mixing of coolant with oil accelerated the oxidation process. The deteriorated oil analysis results correlated with the diagnosed cylinder head damage. Accelerated oil degradation was further exacerbated by contaminants, elevated operating temperatures, and the presence of water, which collectively intensified oxidation. The possibility of measurement errors, such as improper sample collection or misinterpretation of results, was ruled out. The only additional factors that may have worsened the situation were delayed diagnostics and the failure to promptly identify the coolant system leak, leading to continued oil degradation and increased oxidation levels.

The TBN value never reaches the level of fresh oil. The drop in TBN immediately after an oil change is caused by several factors:

• Residual old oil in the system: leftover oil with a significantly lower TBN mixes with the new oil, reducing its initial TBN value.

• Presence of contaminants in the engine: combustion by-products remaining in the engine quickly react with the new oil, causing a decline in TBN at the very start of operation.

• Immediate consumption of alkaline additives: these are used to neutralize residual acids present in the system.

• Engine operating conditions and fuel quality: even fresh oil begins neutralizing combustion by-products as soon as the engine starts.

• Oil mixing within the system: it is often impossible to fully separate old and new oil, especially if the system was partially refilled earlier.

As a result, the TBN value after an oil change never reaches the nominal level of new oil and is typically slightly lower within the first hours of engine operation. Moreover, tests are rarely conducted immediately after engine startup, further explaining why the TBN does not reflect the fresh oil level.

The nitration graph shows a clear upward trend, particularly noticeable after exceeding 10,000 operating hours. Up to around 10,000 h, low nitration values between 0–2 indicate a relatively small presence of nitrogen compounds. Between 10,000 and 30,000 operating hours, a significant increase in nitration values of 7–8 suggests a growing presence of nitrogen-containing products, indicating higher levels of nitrogen oxidation processes. Above 50,000 operating hours, the nitration values stabilize around 4–5, with higher values possibly linked to the breakdown of protective additives, indicating the need for oil replacement.

Based on operating hour data, TAN, TBN, oxidation, nitration, and oil replenishments, it is evident that a form of oil usage optimization was performed during engine operation. This included periodic oil replenishment to maintain proper oil quality and protective properties. The replenishments occurred at intervals of 1000–1700 operating hours, with oil changes based on quality analysis and parameter changes, resulting in the following replacement intervals:

• After 797 h—oil change after the initial run;

• After 11,251 h—oil change during inspection;

• After 5014 h—oil change during inspection;

• After 5198 h—oil change due to reduced TBN and increased TAN;

• After 15,592 h—oil change due to an increase in oxidation from 8 to 25;

• After 10,242 h—oil change during inspection;

• After 27,711 h—oil and engine block replacement during inspection;

• After 346 h—oil change.

Monitoring of oil properties, showing the change in TAN and TBN values as operating hours increase, suggests that these are indicators to assess the need for oil replacement or replenishment. Differences in oil replenishment could indicate that oil usage was adjusted to the engine’s operating conditions, such as load and temperature. High oxidation and nitration values in later periods of operation may signal increased contamination, requiring more frequent replenishment. Optimization actions taken in oil usage, along with ongoing monitoring and adjustment of oil replacement and replenishment strategies, can contribute to further improving engine efficiency and performance.

Based on more than 10 years of engine operation using mineral oil, conclusions were drawn about mineral oil, compared with selected findings from synthetic oil analyses used in a twin cogeneration system [49,50,51]. The summary of results and conclusions related to mineral oil is presented in Table 7.

The analysis of mineral oil parameters at different periods of engine operation is presented in Table 8.

Mineral oil, although more economical, quickly deteriorated in its operational parameters as engine operating hours increased, requiring more frequent top-ups and oil changes. The oil change strategy was based on detailed oil quality analyses and parameter changes, which allowed for the change frequency to be adjusted to actual needs rather than using rigid time- or hour-based intervals. Oil changes were performed both when the critical TBN and TAN values were exceeded and when oxidation levels increased, allowing effective prevention of premature engine component wear. Regular monitoring of oil viscosity at both 40 °C and 100 °C served as an indicator of continued oil stability, translating into effective lubrication and reduced mechanical component wear. After the initial start-up period, the engine required earlier oil changes, suggesting that the adaptation process of the oil to the working conditions was critical in the early stages of operation. A decrease in TBN and an increase in TAN were important decision-making factors, as their deterioration indicated a weakening of the oil neutralization capacity, which could lead to corrosion and contamination accumulation. The results of the analyses of different laboratories revealed some discrepancies, highlighting the need to perform the tests at a single, recommended laboratory to ensure consistency and reliability of the results. Introducing a measurement of the amount of oil added allowed for a more accurate determination of its consumption. According to the recommendations, oil analysis every 1000 operating hours would allow detection and control of gradual changes in its parameters, providing an effective preventive measure, especially with intensive engine operation. Rigorous oil quality monitoring allowed for a rapid response to changes and effective prevention of failures or limiting the impact of damage.

On the basis of laboratory analyses and engine operational events, it can be concluded that the use of mineral oil did not negatively impact engine durability. The oil replenishment procedures were not fully effective, suggesting that complete oil replacement would be a more cost-effective solution, especially with high mileage.

3.2. Prediction of Oil Change Timing Based on Its Quality Criterion

The implementation approach for predicting oil change intervals based on the TBN enables the optimization of maintenance schedules, eliminating unnecessary oil changes while preventing excessive engine wear caused by oil degradation. This method ensures efficient engine operation, extends oil lifespan, and minimizes operational costs. Figure 8 presents a pair plot of multiple parameters related to oil quality and condition over time. Data points are color-coded, with the colors representing successive engine operating periods from the addition of fresh oil to its replacement. Each subplot shows either a histogram or a scatter plot of two parameters, to identify distributions and correlations.

The figure presents a pair plot matrix, which illustrates the relationships between five parameters: EWH, KV100, TAN, TBN, and IND. Each combination of two parameters is visualized as a scatter plot to depict their correlations within the analyzed dataset. On the diagonal of the matrix, density curves for individual parameters are displayed. These plots show the frequency and spread of values for all parameters. The density curves provide insight into the variability and distribution of each parameter across the collected samples. The colors and markers represent different analyzed periods, labeled as No. 1.0 to 7.0. The off-diagonal scatter plots display pairwise correlations between the parameters, enabling the identification of trends, clusters, and relationships.

EWH shows a multi-modal distribution, indicating possible groupings based on different engine or operational conditions. KV100, TAN, and TBN have slightly skewed distributions, suggesting a general trend but with some variability among samples. IND also appears to be skewed, with most values clustered around 0–1, but a few extend into negative values, indicating situations where TAN exceeds TBN, likely indicating oil degradation.

The correlation of EWH versus KV100 is a noticeable positive correlation between engine operating hours and kinematic viscosity, suggesting that as oil ages, it becomes more viscous. The correlation between EWH and TAN is a strong positive correlation, which means that as the oil runs for more hours, its acidity increases. The correlation EWH vs. TBN is a negative correlation, showing that as the oil is used longer, its alkalinity decreases. The correlation EWH vs. IND is a strong negative correlation, indicating that as the oil operates longer, the oil quality coefficient declines. This decline reflects oil degradation as TBN decreases and TAN increases.

The correlation of KV100 vs. TAN is a moderate positive correlation, which means that as the oil becomes more acidic, its viscosity also tends to increase, reinforcing the idea that degradation affects both TAN and KV100. The correlation of TAN vs. TBN is a strong inverse relationship, since an increase in TAN often coincides with a decrease in TBN. This relationship is consistent with the oil neutralizing acids until its reserves are depleted. The correlation of TBN vs. IND is a nearly linear relationship, as expected, since IND is derived directly from TBN and TAN. A decrease in TBN usually leads to a decrease in IND, especially as TAN increases. There are visible clusters, especially in the EWH and TBN distributions, which may suggest differences in oil behavior under various conditions and over time. The color-coded clusters in the EWH vs. TAN and EWH vs. TBN plots could indicate differences in operational cycles or varying engine loads that impact oil degradation rates. The downward trend in the oil quality coefficient (IND) with increasing engine operating hours (EWH) confirms that the acid-neutralizing capacity decreases over time. Instances where IND approaches or falls below zero, meaning TAN exceeds TBN, indicate critical points at which the oil loses its protective properties and requires immediate replacement. The appearance of negative or near-zero IND values in certain samples highlights conditions in which the oil is no longer providing adequate protection, indicating the need for replacement.

The correlation analysis of the measured parameters was performed across all analyzed periods and is presented as heat maps in Figure 9.

On the basis of the correlation matrix, several conclusions can be drawn. There is a strong positive correlation once, a strong negative correlation once, two instances of moderate correlation, and two instances of weak or insignificant correlations. The positive correlation between TBN and IND and the negative correlation between TAN and IND are particularly noteworthy:

• There is a strong positive correlation between TBN and IND (0.87), indicating that these two variables tend to increase or decrease together and that the analyzed system tends to group into specific behavioral clusters;

• There is a strong negative correlation between TAN and IND at −0.85, which means that as one of these variables increases, the other tends to decrease;

• A moderate positive correlation is observed between KV100 and EWH at 0.49;

• There is a moderate negative correlation between TAN and TBN (−0.49), indicating a similar inverse relationship;

• There is an almost zero correlation between EWH and TAN (0.02), which indicates that these two variables are independent of each other. Similarly, there is a very weak correlation between KV100 and TAN (0.15).

Table 9 summarizes the values of coefficients a₀ and a₁, along with parameters to assess their statistical significance (p-value) and the quality of approximation in the form of the coefficient of determination R².

The confidence interval for the determined coefficients was assumed to be 90%. In the initial period of operation, there was a rapid decrease in TBN, and only later did the changes follow a linear trend. Measurements taken within 300 h after the oil exchange were excluded. The criteria for oil replacement related to TAN and TBN levels were met in only three cases. Predictions were made for each of these cases, followed by extrapolation of the results to forecast the expected oil replacement time. The values of the IND parameter, which were used to determine the expected engine working hours between oil changes, are presented in Figure 10 to illustrate their changes.

Based on the analysis of oil replacement results, it can be observed that the forecast oil replacement values differ from the actual results, suggesting that real operating conditions require adjustment based on the predictions obtained. The actual replacement time was 12,048 h, which is 24% longer than the predicted 9187 h. This indicates a potentially higher risk of engine damage during the initial operation period. In the second period, the actual replacement time (5014 h) was 56% less than the predicted 7822 h. This significant difference affected the economic efficiency and operational costs of the cogeneration system. The actual operating period shows a small difference (+13%) relative to the predicted working hours. In the fourth case, the actual replacement time (15,592 h) was 78% shorter than the forecast. Such a large discrepancy can indicate serious engine problems, excessive oil consumption, or unjustified oil replacement, leading to substantial operational and environmental costs. The fifth actual replacement period shows a close match between the actual and predicted values, suggesting a correct oil replacement interval. In the sixth period, the actual value was half of the prediction, indicating significant irregularities in the oil replacement timing. In the seventh period analyzed, the engine was still in operation. The analysis highlights the differences between the actual and forecast oil replacement intervals. To improve operational efficiency and reliability, it is necessary to align actual oil replacements with forecasts based on observations and oil condition monitoring procedures.

The predictive method for engine oil degradation during the operational period in cogeneration modules allows effective planning of cogeneration system management, resulting in operational savings and environmental preservation by reducing waste production in the form of used engine oil.

GWP, expressed in kg CO₂ eq., was estimated based on the literature data. Using available information, the excess volume of hazardous waste generated from prematurely discarded mineral oil was also calculated in cubic meters [m³]. The results provide an assessment of the environmental impact across all stages of the oil’s life cycle, including production, transport, usage, and disposal, as presented in Table 10.

The LCA value (3100 kg CO₂ eq.) was estimated based on the average results obtained from a review of the literature. This approach ensures a balanced and representative assessment of the environmental impact of mineral lubrication oil across its life cycle stages. Extending the operational life of mineral oil in cogeneration systems directly enhances system efficiency, engine stability, and maintenance cost reduction. By precisely monitoring lubricating properties—such as viscosity, physicochemical stability (TBN and TAN), wear metals (Fe, Cu, Al), and oxidation products—oil change intervals can be accurately predicted. This minimizes unnecessary service interruptions and reduces the risk of oil degradation leading to failures. Extending service intervals by 37% improves system availability for continuous operation, boosting economic efficiency and increasing the production of electrical and thermal energy. Predictive analysis of oil degradation avoids unplanned maintenance and ensures optimal system performance throughout the oil’s service life. Efficient oil utilization lowers material costs associated with oil and filter replacements, reducing expenses from approximately 3021 to 1887 EUR per machine hour. According to literature estimates, prematurely consumed oil results in a GWP of 68 × 10³ kg CO₂ eq. and generates 18.2 m³ of unnecessary mineral oil waste. The combined reduction in operational costs, resource usage, and environmental impact improves overall economic efficiency while maintaining engine durability and reliability.

In summary, optimizing oil change intervals in cogeneration systems enhances operational performance, reduces environmental impact, and lowers costs. This approach marks a significant step toward sustainable, economically efficient, and environmentally responsible cogeneration system operation.

3.3. Summary of the Impact of Engine Oil Properties on Engine Operation and Criteria for Predicting Oil Change Intervals

Analyses have shown that regular monitoring of oil quality in cogeneration systems is essential to ensure efficient engine operation. Implementing a predictive system based on oil properties enables a better adjustment of oil change schedules to actual needs, directly enhancing the efficiency and reliability of the entire system. These findings highlight the importance of effective oil quality management as a core component of the operational strategy in cogeneration systems [50]. Regular oil quality analysis and monitoring are crucial to operational efficiency, extending engine life, and reducing operational costs. Maintaining the appropriate oil parameters contributes to improved engine energy efficiency, which is significant from both economic and environmental perspectives. Effective management of mineral oil, including regular changes and top-ups, can help minimize environmental impact and increase engine longevity.

Based on data and analyses concerning the operation of mineral oil in cogeneration engines, the following conclusions can be drawn on the operational efficiency, energy efficiency, and environmental impact:

• Operational efficiency

• Decreasing TBN values and increasing TAN values indicate oil degradation, which reduces its ability to protect the engine. This highlights the need for regular oil analyses and replacements to maintain optimal lubrication quality;

• Introducing periodic oil top-ups based on qualitative analyses enables oil replacement optimization, contributing to longer engine life and reduced operational costs;

• Consistent monitoring of oil properties, such as TBN, TAN, and oxidation, enables early detection of wear symptoms, helping to prevent failures and reduce repair costs. The metal content in the oil was also analyzed, including aluminum, barium, boron, calcium, chromium, copper, iron, lead, magnesium, molybdenum, nickel, phosphorus, potassium, silicon, silver, sodium, tin, titanium, vanadium, and zinc. The results showed no elevated metal levels in the oil samples, indicating that excessive wear of engine components did not occur.

• Energy efficiency

• Optimizing oil replacement intervals improves the engine’s energy efficiency, as proper lubrication reduces friction and resistance, potentially leading to lower fuel consumption;

• Maintaining crucial oil properties, such as high-temperature viscosity, supports effective heat management within the engine, which is essential to maintain energy efficiency.

• Environmental impact

• Increasing TAN levels and nitrification indicate oil degradation, which can contribute to increased environmental pollution. Regular oil replacement and quality monitoring help reduce the risk of releasing harmful substances into the atmosphere and groundwater;

• Although mineral oil requires more frequent replacement compared to synthetic oil, its lower purchase cost can support sustainable decisions, provided that it is managed responsibly throughout its usage cycle.

• Recommended steps for ongoing operation

• Conduct a routine oil analysis every 1000 operating hours;

• If the oil parameters deteriorate, perform an immediate comparative analysis in the same laboratory;

• For persistent parameter degradation, reduce the oil sampling interval to 500 operating hours until a replacement is made or the analysis results improve;

• Maintain strict analysis protocols in a recommended laboratory to ensure consistency and representativeness of the results.

Verification through industrial practice has shown that even with similar operating parameters and with the efficiency achieved by gas engines, in cogeneration units fueled by natural gas, installations that utilize natural gas as fuel can operate efficiently and produce economic benefits. The selection of mineral oil depends on multiple factors, including equipment type, operating conditions, budget, and user preferences. While synthetic oils are often recommended in engines fueled by natural gas as a result of their superior performance and protection, mineral oils remain viable in specific applications. The results confirm that mineral oil can be used effectively in cogeneration units. A well-structured and meticulous oil quality monitoring policy enables real-time tracking of the oil condition, facilitating proactive responses and preventive measures. This approach helps to avoid costly failures or, in case of breakdowns, minimize the extent of damage. Crucial factors to monitor in these analyses include the following:

• Oxidation;

• Nitration;

• Changes in oil viscosity;

• TAN;

• TBN;

• Presence of contaminants in the oil.

In addition, tracking the volume of oil refills in the engine provides a reliable measure of oil consumption and engine condition.

Failures in the crank–piston system were not attributed to degraded lubrication, and until the overhaul, the engine exhibited no signs of premature wear in the crank–piston system. Additionally, during turbocharger inspections at an authorized service center, no damage was found from improper lubrication. On the basis of laboratory oil analyses and the engine’s operational event records, it can be recommended that the use of mineral oil does not negatively impact engine durability.

The oil replenishment procedures did not consistently produce definitive results, suggesting that this approach does not always produce the desired effects. In the installations examined, between 600 and 1000 L of fresh oil were used, up to 50% of the total oil volume, for oil replenishment. Given that the oil had already been subjected to long hours of operation, a complete oil change would be a more cost-effective measure that would yield better results in the long run.

For continued operation, the following guidelines for engine oil testing and analysis are recommended:

• Routine oil analysis every 1000 h of operation: conduct oil analysis on a regular basis every 1000 running hours;

• Immediate comparative analysis when oil parameters deteriorate: if oil parameters show signs of deterioration, an immediate comparative analysis should be performed, with the condition that this analysis is conducted in the same laboratory. In practice, attempts to engage independent laboratories for secondary analyses have shown discrepancies, raising questions about the representativeness of those results compared to historical data;

• Reduced sampling interval for persistently degraded oil parameters: if oil parameters remain degraded in subsequent analyses, shorten the sampling interval to 500 running hours until the oil is either replaced or significant improvements are observed in analysis results;

• Strict policy for oil analysis in a recommended laboratory: enforcing a rigorous policy of using a recommended laboratory for oil analysis helps ensure data consistency and reliability. Comparison of results with independent laboratories has shown the impact of this choice on the evaluation of oil performance, highlighting the importance of consistent laboratory practices.

In conclusion, implementing these recommended guidelines for routine oil analysis, consistent laboratory practices, and timely actions based on the oil condition will ensure the continued efficient and reliable operation of the engine.

The study demonstrates that extending oil change intervals through real-time monitoring of TBN, TAN, viscosity, and contamination levels significantly improves the operational efficiency and economic performance of cogeneration systems. Optimized oil utilization not only reduces maintenance costs but also minimizes environmental impacts, as reflected in lower GWP and reduced waste generation. Proactive oil quality monitoring, conducted in strict adherence to analysis protocols, ensures early detection of degradation, prevents failures, and prolongs engine lifespan. By implementing predictive oil management strategies, cogeneration systems achieve enhanced reliability, reduced operating costs, and improved energy efficiency. These findings highlight the importance of structured oil monitoring policies and emphasize that mineral oil, when managed effectively, remains a viable and sustainable choice for gas engine applications.

4. Conclusions

The study demonstrates that monitoring selected parameters is essential for predicting oil degradation, optimizing oil change intervals, and ensuring the reliable operation of gas engines in cogeneration systems. The findings emphasize several critical aspects related to engine maintenance, justified by predictive oil analysis calculations: oil change intervals could be extended, in our case by an average of 37%, reducing unnecessary maintenance downtimes and enhancing system availability. Cost reductions were achieved by lowering material expenses for oil and filter replacements, from 3021 to 1887 EUR per machine hour.

From an environmental perspective, optimized oil management significantly reduced the GWP of prematurely consumed oil, which previously reached 68×10³ kg CO₂ eq., and avoided the generation of 18.2 m³ of unnecessary mineral oil waste. The study identified the main factors contributing to oil degradation, including a decreasing TBN and increasing TAN.

A comparison between mineral and synthetic oils confirmed that, while synthetic oils offer superior durability, mineral oils remain a cost-effective alternative when properly monitored and managed. Proactive maintenance strategies, such as routine oil analysis conducted every 1000 operating hours and shortened sampling intervals in cases of parameter deterioration, proved essential for the early detection of oil wear and degradation, enabling timely interventions.

Engine reliability was validated through inspections, which revealed no significant damage to the engine, further confirming that effective oil management practices can sustain engine performance and longevity under demanding operating conditions.

Author Contributions

Conceptualization, K.P. and R.F.; methodology, K.P. and R.F.; software, R.F.; validation, J.D.; formal analysis, W.H. and M.P.-S.; investigation, A.K. and F.K.; resources, K.P.; data curation, A.K. and F.K.; writing—original draft preparation, K.P. and R.F.; writing—review and editing, J.D.; visualization, W.H. and M.P.-S.; supervision, K.P. and R.F.; project administration, K.P. and R.F; funding acquisition, K.P. All authors have read and agreed to the published version of the manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1. System flow diagram.

Figure 2. Right-hand view of the engine: 1. turbocharger; 2. engine; 3. flywheel/clutch; 4. generator; 5. frame; 6. generator nameplate; 7. engine mounting legs; 8. electric heater; 9. bypass stream oil filter; 10. gas and air mixer.

Figure 3. Left-side view of the engine: 1. generator; 2. flywheel/clutch; 3. ignition system; 4. engine; 5. turbocharger; 6. gas and air mixer; 7. prelubrication pump; 8. engine mounting legs; 9. compressed air starter; 10. frame.

Figure 4. The synoptic panel displaying the lubricating oil system includes visual indicators for oil installation.

Figure 5. Oil top-ups and total oil quantity in the system as a function of engine operating hours.

Figure 6. Total Acid Number, Total Base Number and oil top-ups as a function of engine operating hours.

Figure 7. Oxidation and nitration levels as a function of engine operating hours.

Figure 8. Multivariate analysis of oil quality parameters using pair plot visualization.

Figure 9. Correlation matrix containing Pearson’s correlation coefficients for mineral oil.

Figure 10. Summary of data points and approximating functions over successive operating periods for parameter IND depending on EWH.

Table 1

Parameters of the engine and generator analyzed.

Type	Parameter	Value
Engine	Mechanical Power	4085 kW
	Rotational Speed	1000 rpm
	Fuel	Mine gas, 40–70% CH4
	NOx Emissions	500 mg/N m3 for 5% O2
	CO Emissions	300 mg/N m3 for 5% O2
	Number of Cylinders	V16
	Displacement	271.8 dm3
	Piston Stroke	320 mm
	Piston Diameter	260 mm
	Compression Ratio	12.0:1
	Mean Piston Speed	10.7 m/s
	Engine Oil Volume	2.2 m3
	Oil Consumption at Full Load	0.4 g/kWh
	Oil Flow Rate	125 m3/h
	Oil Temperature (inlet/outlet)	70/75 °C
	Coolant Volume	570 dm3
	Coolant Temp (water jacket)	79/90 °C
	Coolant Flow Rate (water jacket)	108.3 m3/h
	Coolant Volume (fuel mixture)	51 dm3
	Coolant Temp (fuel mixture)	40/44.9 °C
	Coolant Flow Rate (fuel mixture)	65 m3/h
Generator	Weight	14,200 kg
	Voltage/Frequency	6300 V/50 Hz
	Efficiency at 100% Load	97.33%
	Electrical Power	4000 kW

Table 2

Classification of approved lubricating oils.

Type of Oil	Ash Content (% by Weight)	Number of Oils
Mineral Oils	≤0.5	38
Synthetic Oils	≤0.5	4
Mineral Oils	0.5–1.0	21
Synthetic Oils	0.5–1.0	0
Total Oils	-	63

Table 3

Basic parameters for mineral oils.

Parameter	Low-Ash Oils	High-Ash Oils
Sulfated Ash Content (% by weight)	0.4–0.57	0.52–1.0
Total Base Number (mg KOH/g)	4.5–7.1	7.1–10.6
Viscosity at 40 °C (mm2/s)	88.0–156.0	105.0–148.0
Viscosity at 100 °C (mm2/s)	13.0–14.9	13.0–15.1

Table 4

Recommended low-ash oil properties.

Property	Value
Sulfated Ash Content (% by weight)	0.52
Total Base Number (mg KOH/g)	5.6
Viscosity at 40 °C (mm2/s)	126.2

Table 5

Basic parameters for mineral oils.

Property	Value	Test Method
Viscosity Grade	SAE 40	-
Sulfated Ash Content (% by weight)	0.5	ASTM D874
Flash Point (°C)	252	ASTM D92
Kinematic Viscosity at 100 °C (mm2/s)	13.2	ASTM D445
Kinematic Viscosity at 40 °C (mm2/s)	126.2	ASTM D445
Pour Point (°C)	–18	ASTM D97
Specific Gravity (15.6 °C/15.6 °C)	0.887	ASTM D1298
Total Base Number (TBN) (mg KOH/g)	5.6	ASTM D2896
Viscosity Index	98	ASTM D2270

Table 6

The engine inspections and maintenance activities.

Activity	Interval (Operating Hours)	Description
Initial Inspection	300	First start-up and after replacement
Daily Inspection	24	Routine checks
Small-Scale Maintenance	4000	Minor maintenance tasks
Medium-Scale Maintenance	8000	Medium maintenance tasks
Extensive Maintenance	16,000	Comprehensive inspection and repair
Intermediate Overhaul	32,000	Partial engine overhaul
Major Overhaul	64,000	Complete engine overhaul

Table 7

New oil properties and comparison with synthetic oil.

Factor	Mineral Oil
TBN of New Oil	Higher TBN (4.8 mmKOH/g) than synthetic oil
TAN of New Oil	Lower TAN (1.36 mmKOH/g) than synthetic oil
Oxidation Resistance	Good, but slightly lower than synthetic oil
Nitration Resistance	Requires more frequent analysis than synthetic oil
Lubrication Efficiency	Good, comparable to synthetic oil
Acid Neutralization Ability (TBN)	TBN decreases faster than synthetic oil at high loads
Low-Temperature Resistance	Lower than synthetic oil, but improved by the heating system
Need for Refreshing	Necessary approximately every 1000 operating hours, with low effectiveness
Impact on Engine Durability	Good, no damage observed
Laboratory Analysis Stability	Differences arise from laboratories, but parameters are stable
Kinematic Viscosity at 40 °C	Higher viscosity (126.2 m2/s) than synthetic oil
Kinematic Viscosity at 100 °C	Compared to synthetic oil (13.2 m2/s)
Purchase Costs	Lower than synthetic oil
Cost of Replacement per 1000 Operating Hours	Lower (more frequent replacements) than synthetic oil
Resistance to Variable Parameters	Mineral oil did not cause problems in the crankshaft–piston system
Laboratory Analyses	Requires accurate studies and comparisons in the same laboratory
Oil Replenishment Processes	Refreshment (up to 50% volume) was more expensive than full replacement; full replacement is recommended in the future
Operational Recommendations	Regular analysis every 1000 operating hours; increase frequency of analysis when oil parameters worsen
Operational Summary	Lower cost than synthetic oil, comparable durability and performance; no premature wear of the crankshaft–piston system observed

Table 8

Summary of results and conclusions related to mineral oil with 0, 5000, and 10,000 operating hours.

Parameter	New Mineral Oil(0 Engine Operating Hours)	Mineral Oil After 10,000 Engine Operating Hours	Mineral Oil After 30,000 Engine Operating Hours
TBN Number	5.6	4.8 (decline more intense than synthetic)	3.5 (necessary replacement)
Viscosity at 40 °C	126.2	130 (slight increase)	138 (lower stability at higher rbh)
Viscosity at 100 °C	13.2	13.5	13.8 (upward trend)
Oxidation Resistance	Medium	Moderate	High oxidation
Nitration Resistance	Moderate	Reduced	High nitration
Purchase Cost	Low	-	-
Cost of Replacement per 1000 rbh	Low	Low (replacement needed due to higher wear)	Low (more frequent replacements)
Lubrication Efficiency	Good	Moderate	Reduced (replacement needed)
Acid Neutralization Capacity (TBN)	Medium	Reduced	Low (requires replacement)
Low-Temperature Resistance	Medium	Moderate	Reduced (difficult startups without heating)
Need for Refreshing	More frequent with higher engine operating hours	Refreshing every 1000 engine operating hours	Requires additional top-ups
Engine Durability Impact	Good durability	Good, but reduced lubrication at high engine operating hours	Reduced, potentially higher wear
Analysis Result Stability	Moderate	Reduced	Monitoring of different laboratories needed
Maintenance Interval Recommendation	Shortened intervals, monitoring recommended	Shortened intervals	Shortened intervals with analyses for prevention

Table 9

Summary of results of statistical parameters and predicted engine working hours of oil exchange.

No.	a0	p-val.	a1	p-val.	R2	EWH (h)		Difference (%)
						Actual	Predicted
1	2.952	0.0011	−0.294	0.0120	0.5226	12,048	9187	31
2	3.233	0.0001	−0.381	0.0011	0.9977	5014	7822	−36
3	4.444	0.0051	−0.929	0.0119	0.9764	5198	4516	15
4	2.781	0.0000	−0.072	0.0712	0.2289	15,592	34,980	−55
5	2.034	0.0035	−0.164	0.0934	0.3118	10,242	10,880	−6
6	2.634	0.0000	−0.056	0.0004	0.5068	20,388	42,262	−55
7	3.151	0.0000	−0.105	0.0414	0.4240	--	27,541	--

Table 10

GWP contribution for mineral oil life cycle stages by literature review (per 1 t).

Life Cycle Stage	Parameter	GWP Value (kg CO2 eq.)
Production	Emissions from extraction and refining	1500–2000
Transport	Emissions (500 km average distance)	50–150
Usage	Degradation and emissions in engines	500–1300
Disposal/Recycling	Emissions from burning/recycling oil	200–500
Sum		2250–3950

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