Process Simulation and Optimization of Anaerobic Co-Digestion

Abstract

Anaerobic digestion (AD) is an established method that has been extensively utilized for waste management, waste treatment, and biogas production. Anaerobic co-digestion (AcoD) is regarded as a practical approach to address substrate characteristics and system optimization issues. The distinction between AcoD and mono-digestion is that AcoD has a higher organic loading and significant substrate composition variation. There are many factors involved in AcoD which affect the biogas plant’s production ability and performance. Using response surface methodology (RSM) to forecast the optimal conditions for maximum biogas output, this article provides an overview of the different operational parameters in the AcoD process, modeling of the AcoD process, and overall process optimization. Standard software used for AcoD process simulation are Aspen Plus, SuperPro Designer, BioWin, CFD, and MATLAB. Review addresses design, development, and optimization frameworks for biogas production systems that take numerous aspects into account. The most significant AcoD optimization parameters include temperature, co-substrate concentration, inoculum ratio (percent), and C/N ratio.

Analogous study of cumulative biogas production by anaerobic digestion of sewage treatment plant sludge, the proposal of universal dimensionless models

“…A comparison was made between the proposed models (Equations 22 and 23) and the best model found for each product selected in the literature; Equation (20) model for Gelatin, Residual organic matter, and for sludge using the coefficient of determination, and root mean square error is indicated in Table 9. As a result, the proposed models (Equations 22 and 23) have higher values of r 2 and lower values of RMSE than those obtained using Equation (20) from the scientific literature.…”

“…The measured biogas was higher by 2.5‐fold than that predicted by simulation. Successful simulation studies were carried out on the anaerobic codigestion process to find the optimal ratios of substrates in the suspension introduced into the digester by Inayat et al 20 The optimal substrate composition found is 50% waste‐water, 25% livestock manure, and 25% biomass. The development of a process modeling simulation by Aspen Plus, for the anaerobic digestion process, was investigated by Al‐Rubaye 21 .…”

“…23 The proposed approach overcomes various limitations in the reviewed works, which is the impetus of the proposed work. 24 Most of the previous articles have partially addressed effectiveness evaluation with simple models, such as the response surface methodology (RSM) 20 and the AD model developed in Aspen. 22 However, proposed empirical models, capable of addressing most of the problems addressed and serving as a tool to meet future requirements for biogas production, are limited in the literature.…”

Response surface methodology for process optimization in livestock wastewater treatment: A review

“…Such implications highlight the importance of optimizing AD and CD processes to treat LWW. Compared to the conventional optimization approach (OFAT), RSM-optimized systems showed better performance in terms of CH 4 yield during AD and CD of LWW [ 86 , 87 ]. Hence, several studies applied RSM to optimize key operating parameters such as temperature, hydraulic retention time (HRT), pH, substrate concentration, mixing ratio, total solids content, and treatment time in anaerobic digestion and co-digestion of LWW to achieve maximum biogas production along with organic matter removal efficiency ( Table 3 ).…”

“…Furthermore, Gadhe et al [ 88 ] employed a BBD-RSM design to increase the hydrogen production potential from swine wastewater by optimizing substrate concentration, pH, COD/N, and COD/P ratios. Along with improving biogas productivity and pollutant removal efficiency, optimization of biological LWW treatment processes can help evaluate the process performance, save time, and reduce operational costs [ 87 , 89 ]. So, RSM can serve as an effective tool to investigate the interactions between different factors and their impacts on biogas production, nutrient removal, and process stability in biological LWW treatment.…”