An enhanced anaerobic digestion BioModel calibrated by parameters optimization based on measured biogas plant data

Improvement of Biogas Production Utilizing a Complex Anaerobic Digestion Model and Gradient-Based Optimization

“…The daily variations in FW, PM, and CS fractions in the F-CS are shown in Figure 2a for a period of one year, while the corresponding data for CM, FM, and W are presented in Figure 2b. The composition of each substrate of the F-CS, Table 1, was determined by the methods described in the corresponding standards [6]. To determine the biogas composition, the IR Gas Analyzer GA2000 plus was used.…”

“…To simulate the AD process, an in-house developed complex BioModel, which is described in detail in [1,6], was used. To summarize briefly, this model takes into account chemical (acid-base reactions), biochemical (hydrolysis, acidogenesis, acetogenesis, and methanogenis), and physico-chemical (liquid-gas and liquid-solid mass transfers) processes, where carbohydrates, proteins, and lipids degrade into CH 4 and many other by-products.…”

“…), additives (inoculum, trace elements, enzymes, etc. ), and AD process conditions [5,6]. Among others, this means that by simultaneously digesting two or more organic substrates, a more favorable composition of the substrate for the AD process can be obtained.…”

“…All these mentioned possibilities are investigated not only experimentally but also by numerical simulations, which provide a useful tool for AD process understanding and optimization. To better understand the AD process, reliable mechanistically inspired models for the numerical simulation of processes in an anaerobic bioreactor, based on ADM1 and BioModel, seem to be the preferred option [1,6,[18][19][20][21][22][23][24]. So far, AD process optimization has been mostly performed using the genetic algorithm, particle swarm, and ant-colony algorithms [25][26][27], while gradient-based optimization methods were engaged relatively rarely [28,29].…”

“…Elagroudy et al [28] used a gradient-based optimization method in order to find the best empirical kinetic model for the simulation of biogas production; various kinetic and performance parameters of several models (modified Gompertz, logistic model, reaction curve-type, and exponential rise to maximum) were calibrated. Furthermore, a high number of model parameters in a mechanistically inspired BioModel were calibrated by an approximation-type gradient-based optimization method quite efficiently [1,6]. A gradient-based algorithm also proved to be stable and efficient in determining AD process parameters (temperature and pH value) when using a simple mechanistically inspired model [29].…”