A review of response surface methodology for biogas process optimization - Solal Stephanie Djimtoingar; Nana Sarfo Agyemang Derkyi; Francis Atta Kuranchie; Joseph Kusi Yankyera (Cogent Engineering, 2022)
This paper aimed at reviewing current researches on the use of Response Surface Methodology in the optimisation of Biogas processes. It explored the performance of RSM in biogas process optimization, the most effective technique and the attendant effective software used in such processes. It attempted to review literature in the area. 55 articles were systematically reviewed. The online databases included were Google Scholar, Scopus and other statistics-based optimization research databases with keywords from Response Surface Methodology in Biogas Optimization. The review finds that RSM proves to be an effective statistical tool. It has achieved optimum objectives for biogas production: increased biodegradability, optimum biogas yield and methane production, increased Total Solid and reduced Volatile Solids and an increased COD removal. The key advantage of RSM was found to be a reduced number of experimental trials, making it time and cost-effective. 37 process parameters have been optimised using RSM, over the last two decades. Five of these parameters are dominant. Namely,: Temperature, pH, Retention time, Pre-treatment and Loading rate. The major challenges associated with the use of RSM in biogas production process optimization are the limited experimental range. Techniques to combine RSM with other optimization methods such as the Taguchi, Kriging or the Artificial Neural Network (ANN) are being developed to address these challenges. Design Expert software is the most used software because of its low cost of use. However, Statistica offers a better efficiency.