Multiple model-based control of multi variable continuous microbial fuel cell (CMFC) using machine learning approaches

Yewale, Ashish; Methekar, Ravi; Agrawal, Shailesh

doi:10.1016/j.compchemeng.2020.106884

Cited by 19 publications

(8 citation statements)

References 44 publications

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“…AI can picture retrobiosynthesis (a reverse engineering-like approach, where metabolism can be disentangled in terms of originating reactions and their relationship) approaches, highlighting key reaction rules present in biological systems [ 93 ]. Moreover, machine learning techniques are used to incorporate genomic data for predicting the optimal feed substrate of MFCs [ 94 ], while machine learning can be used for modelling and controlling the temperature inside those cells [ 95 ].…”

Section: Discussionmentioning

confidence: 99%

Research Trends and Future Perspectives in Marine Biomimicking Robotics

Aguzzi

Costa

Funari

et al. 2021

Sensors

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Mechatronic and soft robotics are taking inspiration from the animal kingdom to create new high-performance robots. Here, we focused on marine biomimetic research and used innovative bibliographic statistics tools, to highlight established and emerging knowledge domains. A total of 6980 scientific publications retrieved from the Scopus database (1950–2020), evidencing a sharp research increase in 2003–2004. Clustering analysis of countries collaborations showed two major Asian-North America and European clusters. Three significant areas appeared: (i) energy provision, whose advancement mainly relies on microbial fuel cells, (ii) biomaterials for not yet fully operational soft-robotic solutions; and finally (iii), design and control, chiefly oriented to locomotor designs. In this scenario, marine biomimicking robotics still lacks solutions for the long-lasting energy provision, which presently hinders operation autonomy. In the research environment, identifying natural processes by which living organisms obtain energy is thus urgent to sustain energy-demanding tasks while, at the same time, the natural designs must increasingly inform to optimize energy consumption.

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Section: Discussionmentioning

confidence: 99%

Research Trends and Future Perspectives in Marine Biomimicking Robotics

Aguzzi

Costa

Funari

et al. 2021

Sensors

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“…ML has been reported to switch MFC models from algorithms including RBA, WkNN, GMA, etc. For instance, Yewale et al [39] proposed an MMB controller strategy to solve the nonlinearity problem in CMFC and implemented it on the developed MIMO system. For MMB controllers, the model-switching approach needs to work precisely on the overlap of multiple subspaces created by decomposing operational regimes into ICE: integral control command; CE: control command).…”

Section: Switching Modelsmentioning

confidence: 99%

“…Average performance criteria for MMB controller's multiple switching mechanisms (ST: stability time; ISE: integral square of error;ICE: integral control command; CE: control command). Reproduced with permission [39]. Copyright 2020, Elsevier.…”

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confidence: 99%

Machine Learning in Bioelectrocatalysis

Huang,

Gao,

Chang

et al. 2023

Advanced Science

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At present, the global energy crisis and environmental pollution coexist, and the demand for sustainable clean energy has been highly concerned. Bioelectrocatalysis that combines the benefits of biocatalysis and electrocatalysis produces high‐value chemicals, clean biofuel, and biodegradable new materials. It has been applied in biosensors, biofuel cells, and bioelectrosynthesis. However, there are certain flaws in the application process of bioelectrocatalysis, such as low accuracy/efficiency, poor stability, and limited experimental conditions. These issues can possibly be solved using machine learning (ML) in recent reports although the combination of them is still not mature. To summarize the progress of ML in bioelectrocatalysis, this paper first introduces the modeling process of ML, then focuses on the reports of ML in bioelectrocatalysis, and ultimately makes a summary and outlook about current issues and future directions. It is believed that there is plenty of scope for this interdisciplinary research direction.

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“…compared with the single model linear controller when a multiple models-based control strategy is used. A weighted k-nearest neighbor was the best machine learning algorithm in terms of performance [73].…”

Section: Models Produced In the Mggp Model Have Shown Excellent Abili...mentioning

confidence: 99%

A Survey of Applications of MFC and Recent Progress of Artificial Intelligence and Machine Learning Techniques and Applications, with competing fuel cells

et al. 2022

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Tomorrow is a technology for Microbial fuel cells (MFC). It has attracted numerous studies for the continuous development of cell efficiency since the problem of the coming era can be resolved. Implementing artificial learning and machine learning is a change that can effectively achieve the goals. A microbial fuel cell constitutes a complex non-linear procedure that preferably needs a strategy that is not a linear control strategy for the most optimum result. Rather than making a computationally tedious and heavy non-linear control strategy, a superior single linear model or gain scheduling, or multiple models-based control techniques are the practical and feasible ways to tackle the non-linearity existing in the Microbial Fuel Cell. Machine learning and Artificial Intelligence help reduce computation and model costs. It saves time and is more efficient than previously used manual methods, which are now obsolete. In order to find the most accurate results, the study would compare all currently available research efforts and focus on implementing Artificial Intelligence and Machine learning concepts within the Microbial Fuel Cell and comparison with other fuel cells.

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Multiple model-based control of multi variable continuous microbial fuel cell (CMFC) using machine learning approaches

Cited by 19 publications

References 44 publications

Research Trends and Future Perspectives in Marine Biomimicking Robotics

Research Trends and Future Perspectives in Marine Biomimicking Robotics

Machine Learning in Bioelectrocatalysis

A Survey of Applications of MFC and Recent Progress of Artificial Intelligence and Machine Learning Techniques and Applications, with competing fuel cells

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