Analyze mathematical model for optimization of anaerobic digestion for treatment of waste water. (2022)

• Record Type: Journal Article
• Authors: Mathur, Prashant; Singh, Sudhanshu
• Is Part Of: Materials today. Volume 62:Part 8(2022)
• Page Start: 5575
• Page End: 5582
• Publication Date: 2022
• Subjects: Anaerobic Digestion -- Artificial intelligence Techniques -- Machine Learning Techniques -- Artificial Neural Network -- Waste Water Treatment; Materials science -- Congresses -- Periodicals; 620.1
• DOI: 10.1016/j.matpr.2022.04.606
• Languages: English
• ISSNs: 2214-7853

Abstract

Abstract: Waste management is a critical open issue in today's era of large-scale urbanization. Waste management Technologies such as aerobic and anaerobic digestion, not only does it aid in garbage recycling, but it also contributes to the production of green energy in the form of biogas. Anaerobic digestion produces methane, which can be used in a variety of applications including the natural gas grid, boilers, vehicle fuel, kitchen stoves and combined heat and Power Systems. Different specifications or characteristics of biogas are required in these applications. Extensive literature is available regarding correlation of substrate and the characteristics of biogas produced. Control systems for anaerobic digestion have not received an equal research attention. Anaerobic digestion is a highly sensitive process and depends upon several environment and operational factors. However, an aerobic digestion plants are designed on the basis of static or fixed operating conditions. This approach is not practical as the output of an aerobic digestion changes with change in in both flow and load. The lack of Optimization strategies results in decline of performance. The aim of this research work is to present state of art of an aerobic digestion Modelling, optimization and control strategies. The papers summarize mathematical techniques for modeling an aerobic digestion. Different optimization strategies are also discussed. Finally use of machine learning and soft computing techniques control of aerobic digestion plant is discussed and recommendations for future research work are enlisted.