Volume 29, Issue 4

Microbial fuel cells (MFCs) are an emerging technology that has gained considerable attention in the recent years because they provide new opportunities for sustainable production of energy from a wide range of soluble complex organic wastes and renewable biomass. The driving force for research in this field has been the apprehension over the energy climate crisis and environment pollution. MFCs are bioreactors that can convert the chemical energy present in organic compounds into electrical energy. Presently, the literature shows that current and power yields are relatively low, but improvements in the technology can enhance these parameters as well as the efficiency of these cells. Sediment MFCs in powering low-powered electronic monitoring devices is one of the practical uses of MFCs. Additionally, MFCs can be used in implantable medical devices and wastewater treatment plants. This review discusses the factors governing the performance of these cells and the maximum power density that can be obtained using various combinations of substrates and microorganisms.

Artificial neural networks (ANN) provide a range of powerful new techniques for solving problems in sensor data analysis, fault detection, process identification, and control and have been used in a diverse range of chemical engineering applications. This paper aims to provide a comprehensive review of various ANN applications within the field of chemical engineering (CE). It deals with the significant aspects of ANN (architecture, methods of developing and training, and modeling strategies) in correlation with various types of applications. A systematic classification scheme is also presented, which uncovers, classifies, and interprets the existing works related to the ANN methodologies and applications within the CE domain. Based on this scheme, 717 scholarly papers from 169 journals are categorized into specific application areas and general (other) applications, including the following topics: petrochemicals, oil and gas industry, biotechnology, cellular industry, environment, health and safety, fuel and energy, mineral industry, nanotechnology, pharmaceutical industry, and polymer industry. It is hoped that this paper will serve as a comprehensive state-of-the-art reference for chemical engineers besides highlighting the potential applications of ANN in CE-related problems and consequently enhancing the future ANN research in CE field.