Comparison of Optimisation Algorithms for Centralised Anaerobic Co-Digestion in a Real River Basin Case Study in Catalonia

Abstract

Anaerobic digestion (AnD) is a process that allows the conversion of organic waste into a source of energy such as biogas, introducing sustainability and circular economy in waste treatment. AnD is an intricate process because of multiple parameters involved, and its complexity increases when the wastes are from different types of generators. In this case, a key point to achieve good performance is optimisation methods. Currently, many tools have been developed to optimise a single AnD plant. However, the study of a network of AnD plants and multiple waste generators, all in different locations, remains unexplored. This novel approach requires the use of optimisation methodologies with the capacity to deal with a highly complex combinatorial problem. This paper proposes and compares the use of three evolutionary algorithms: ant colony optimisation (ACO), genetic algorithm (GA) and particle swarm optimisation (PSO), which are especially suited for this type of application. The algorithms successfully solve the problem, using an objective function that includes terms related to quality and logistics. Their application to a real case study in Catalonia (Spain) shows their usefulness (ACO and GA to achieve maximum biogas production and PSO for safer operation conditions) for AnD facilities.

Biogas Production Optimization in the Anaerobic Codigestion Process: A Critical Review on Process Parameters Modeling and Simulation Tools

“…In addition, GAs can be applied to optimization problems where the underlying system is treated as a black box, requiring no detailed knowledge of its internal workings. Tis characteristic makes GAs suitable for real-world applications in which the system's behavior may be complex or not fully understood [104]. Despite these benefts, GAs have some limitations in the context of predicting biogas production, such as dynamic changes in substrate composition, microbial populations, and environmental conditions associated with AcoD.…”

“…GAs often requires problem-specifc tuning to attain optimal results, which can make them less user-friendly for practitioners lacking expertise in algorithm parameterization [105]. However, the algorithm's performance can be afected by the selection of initial parameters, and identifying an optimal parameter set may entail additional computational efort [104]. In addition, the efectiveness of GAs can be afected by how the problem is represented, such as the encoding of parameters and the selection of a specifc genetic operator.…”

Advanced Anaerobic Digestion With Optimization Techniques Using Genetic Algorithm and Fuzzy Logic

“…Optimization and fuzzy control methods relevant to this study are presented in Section 4. Part 5 presents the findings, analysis, and final thoughts (1)(2)(3)(4) .…”

Advances in the Monitoring, Diagnosis and Optimisation of Water Systems

“…Work presented in [ 5 , 6 , 7 , 8 ] discusses the water-sanitation side. In this field, there is a growing interest in the adaptation and use of technologies related to the circular economy which promote environmental sustainability, where resource recovery is a key issue for industrial and environmental processes and involves a wide spectrum of study possibilities.…”

“…Due to their potential for resource recovery and the further implications in the water–food–energy nexus, WWTPs have been a research focus in different areas of expertise: from modelling and engineering design to process dynamics, simulation, and integration. This line of work is introduced in [ 6 ], where resource recovery—namely biogas in the latter reference—is optimised by a centralised codigestion method considering real data from a WWTP network. Different nature-inspired optimisation algorithms are compared in the performance of this task, providing potential dramatic improvement when compared with actual nonoptimised operation.…”