Consideration of biological and inorganic additives in upgraded anaerobic digestion BioModel

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

“…In the quest to reduce waste and produce energy, the anaerobic digestion (AD) process plays an important role [1,2]. AD is a very complex process, including hydrolysis, acidogenesis, acetogenesis, and methanogenesis, where the activity of various types of microorganisms in an oxygen-free environment degrades carbohydrates, lipids, and proteins from organic wastes into biogas [3,4].…”

“…Consequently, a more stable AD process, as well as enhanced biogas and CH 4 production, can be achieved. Furthermore, the AD process's parameters, such as temperature, pH value, and additives, highly influence the formation and quality of the produced biogas and digestate [1,2]. The AD process can be performed under psychrophilic (10-25 • C), mesophilic (25-45 • C), and thermophilic (45-65 • C) temperature conditions [1,7].…”

“…Furthermore, the AD process's parameters, such as temperature, pH value, and additives, highly influence the formation and quality of the produced biogas and digestate [1,2]. The AD process can be performed under psychrophilic (10-25 • C), mesophilic (25-45 • C), and thermophilic (45-65 • C) temperature conditions [1,7]. Temperature affects the microorganism community, processes stability, and AD products.…”

“…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].…”