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This study involved implementing an instrumentation, control, and monitoring system to analyze a biogas production plant from agro-industrial waste. The system included temperature, level, and pressure sensors installed at various points of the process, all connected through a Programmable Logic Controller (PLC) device and controlled via TIA PORTAL using proportional integral derivative (PID) control strategies. A Siemens KTP700B HMI (Human-Machine Interface) display provided real-time visualization of plant components, while the S7-1200 PLC enabled data acquisition. Power BI software was used for remote monitoring with trend graphs, ensuring comprehensive performance oversight.
The system kept a stable temperature of 43 °C during the anaerobic digestion process, while pressure fluctuated between 30 to 120 PSI due to biogas production, reaching a substrate volume of 500 liters. The HMI also allowed for manual recording of critical data related to biogas production, including variations in CH4 and CO2 concentrations. Initially, methane levels were higher than those of carbon dioxide. However, during a specific phase, the CO2 concentration increased significantly, while CH4 levels decreased. This change coincided with a deliberate plant shutdown designed to assess the impact of automation. During this shutdown, temperature, feed, and pH controls were temporarily halted. Once these controls were reactivated, methane levels began to gradually increase, once again surpassing CO2 levels.
Furthermore, the real-time collection of data on parameters such as temperature, pressure, pH, alkalinity, volatile fatty acids, and chemical oxygen demand allowed the timely application of corrective actions to address system disturbances effectively
Details
Proportional integral derivative;
Alkalinity;
Internet of Things;
Data acquisition;
pH;
Anaerobic digestion;
Man-machine interfaces;
Plant shutdowns;
Anaerobic processes;
Chemical oxygen demand;
Biogas;
Programmable logic controllers;
Carbon dioxide;
Methane;
Remote monitoring;
Carbon dioxide concentration;
Real time;
Industrial wastes;
Data collection;
Instruments
