A review of response surface methodology for biogas process optimization

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, Pretreatment 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.

Automated summary

This paper reviews the application of Response Surface Methodology (RSM) in the optimization of Biogas processes. It finds that RSM is an effective statistical tool for optimizing biogas production, achieving objectives such as increased biodegradability, optimum biogas yield, and improved COD removal. The major advantages of RSM are its ability to reduce the number of experimental trials and its cost-effectiveness. The review also identifies the challenges associated with limited experimental range and discusses the development of techniques that combine RSM with other optimization methods. The most commonly used software in this field is Design Expert, although Statistica offers better efficiency.