Estimation of Biogas Production Rate in a Thermophilic UASB Reactor Using Artificial Neural Networks

Abstract

Biogas production rate was modeled and estimated in a thermophilic upflow anaerobic sludge blanket digester. Data set covers a time period of both steady-state conditions and an abnormal operation condition, i.e., organic loading shocks. Multilayer neural networks topology was used as the modeling tool. Half of the experimental data were used for the training of the model and the remaining half were used for the testing stage. Model results were evaluated from the point of view of both steady conditions and abnormal conditions. It was seen from the time series trends of the estimated data that biogas production rates at steady state operation conditions were closely estimated by the model while the results for organic loading shocks were sufficiently followed. Artificial neural network models gave encouraging estimation results for the online control of thermophilic reactors.

Application of artificial neural networks in modeling and optimization of biofuels production

“…Biogas production Biogas production rate was modeled and estimated in thermophilic UASB (Upflow Anaerobic Sludge Blanket) reactor (Kanat and Saral, 2009). Experimental data cover stationary state period and period of unusual process conditions -organic substrate addition.…”

New explicit formulations for accurate estimation of aeration-related parameters in steady-state completely mixed activated sludge process

“…In the planning stage of modeling and simulation-based studies, selection of the most appropriate model components is a crucial factor in order to recognize possible technical faults and to reduce computation time, as well as to develop an accurate modeling methodology for a specific environmental process (Yetilmezsoy et al, 2015;Kanat and Saral, 2009;Yetilmezsoy and Sapci-Zengin, 2009). For the present case, the model variables and their respective ranges were chosen in accordance with the relevant literature (Yetilmezsoy, 2010;Yetilmezsoy, 2016;142 YETILMEZSOY K. Orhon and Artan, 1994;Muslu, 1996a;Muslu, 1996b;Qasim, 1998;Crites and Tchobanoglous, 1998 ), X10 = Ta: ambient air temperature (°C), X11 = Ti: influent wastewater temperature (°C), X12 = H: static pressure caused by wastewater depth in the areation basin, measured in head of water (m), and X13 = Ha: elevation or altitude above sea level (m).…”

Effect of extracellular enzyme activity on digestion performance of mesophilic UASB reactor treating high-strength municipal wastewater

“…It is essential that the reactor contents provide enough buffer capacity to neutralize any eventual VFA accumulation and thus prevent build-up of localized acid zones in the digester [47]. In the process, the degradation of proteins in the wastewater by anaerobic treatment results in generation of alkalinity due to the reaction of ammonia with carbon dioxide and water [48,49].…”

“…In the process, the degradation of proteins in the wastewater by anaerobic treatment results in generation of alkalinity due to the reaction of ammonia with carbon dioxide and water [48,49]. However, at low initial alkalinity in the reactor, the decrease in pH will be larger at the increased VFA concentrations [47]. More importantly, high VFA concentrations can be detrimental particularly for the biological activity of the methanogens [50].…”