Mathematical modeling in biochemical engineering as a tool for the comprehension of the anaerobic digestion process

Abstract

The anaerobic digestion process has been partly improved due to biochemical engineering, a modern interdisciplinary field emphasizing biological processes. Anaerobic digestion has grown in relevance over time, primarily because of its low energy input and capability to biotransform organic matter, contained in residues and by-products with low added value into bioenergy (H2 or CH4) and bioproducts (organic acids, ethanol, biopolymer, biofertilizer, and reuse water). Modeling emerges, in this context, as an enabling tool for the rationalization and the comprehension of anaerobic digestion, as well as for the optimization and control of this bioprocess for the treatment of biodegradable waste. This chapter's purpose is to provide a general overview of mathematical modeling in the context of biochemical engineering applied to anaerobic digestion. It does so by addressing and discussing the following topics: (i) introduction to biochemical process engineering; (ii) introduction to mathematical modeling applied to biochemical processes; (iii) fundamentals of mathematical modeling of biochemical processes, including the description of mathematical models elaboration and classification; (iv) biochemical process engineering and mathematical modeling; (v) process or equipment design and mathematical modeling; (vi) mathematical models applied to the fundamental and technical analysis of anaerobic digestion; and (vii) educational aspects in the teaching and learning of mathematical modeling. Furthermore, this chapter examines operational analysis and the main performance indicators of anaerobic digestion, current research on the field, and the methodology of mathematical models used to investigate fundamental and technological aspects of anaerobic digestion. The methods included were stoichiometric, kinetic, thermodynamic, mass transfer, scale-up and energy production estimation, and preliminary economic analysis. The chapter closes with a discussion of cognitive and critical thinking abilities crucial to teaching, learning, and practicing mathematical modeling. © 2024 Nova Science Publishers, Inc.