The operational conditions of the anaerobic bioreactor can be predicted using three parameters, such as biogas pressure, biogas mass flow rate, and methane gas content. This research will present a monitoring system for the operational conditions of an anaerobic bioreactor through the measurement of those operational parameters based on knowledge data. The knowledge-based monitoring system is equipped with an interface for connectivity with pressure sensor, mass flow rate and methane gas content sensor, so that it can monitor the condition of the bioreactor in real time. The implementation of bioreactor monitoring is applied at bioreactors in Pakal Benowo Forest, Surabaya. The trial test was carried out under three different conditions: In the morning (around 08.00 AM) indicated information on the methane gas content of 72.5% LEL, biogas pressure 7.3 kPa and mass flow rate biogas 0.3 liters per minute. The second one during the day (around 11.00 AM) indicates information on the methane gas content of 56.5% LEL, biogas pressure 1.1 kPa and biogas mass flow rate of 0.3 liters per minute. The last, in the afternoon (around 03.00 PM) indicates information on the methane gas content of 51.1% LEL, biogas pressure of 1.1 kPa and biogas mass flow rate of 0.3 liters per minute. From those results, our monitoring system is able to identify the operational conditions of the anaerobic bioreactor.

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The research consists of two parts, the first one is to design the dynamic plant model of polishing unit using artificial neural network (ANN) type backpropagation, and the second one is to design a simulation of a close loop control system on Simulink consisting of logic solver, control valve and ANN polishing unit. The ANN polishing unit was trained and obtained the best model structure 4-24-3 with four inputs chemical oxygen demand (COD) influent, oil in water (OIW) influent, urea, and triple superphosphate (TSP), twenty-four hidden layer nodes, and three outputs (OIW effluent, COD effluent and dissolved oxygen (DO)). The mean square error (MSE) and root mean square error (RMSE) from ANN trained were 0.00485 and 0.06964, obtained by the second iteration. From the simulation results on Simulink by giving several scenarios in the logic solver condition table, the action is brought in the form of urea and TSP nutrition issued by the control valve. The values are used to achieve the DO setpoint (2 mg/L), among others: when COD and OIW influent exceed the quality standard, COD exceeds the quality standard, and OIW does not exceed the quality standard, and the DO error is below zero.

Incomplete combustion process from boiler in palm oil processing industry usually discharged into the air in the form of black smoke due to the high water contain in the fuel of the boiler which will pollute the air. With this problem the research was conducted of designing a simulation of fuel dryer to dry out the boiler fuel before it used by utilizing the exhaust heat of the combustion system in the boiler. The simulation design of the fuel dryer is based on computational fluid dynamic (CFD) with parameters control are air velocity and heat temperature of fuel dryer. The results obtained the distribution of air velocity in the dryer has a value range of 0-40 m/s with the highest velocity occurring when hot exhaust air enters the dryer while the temperature distribution inside the dryer has a value range of 90-270 °C with the highest temperature appearing around hot air inlet exhaust and the lowest temperature appears in the hot air outlet section of the dryer.