Machine learning methods for the modelling and optimisation of biogas production from anaerobic digestion: a review

Ling, Jordan Yao Xing; Chan, Yi Jing; Chen, Jia Win; Chong, Daniel Jia Sheng; Tan, Angelina Lin Li; Arumugasamy, Senthil Kumar; Lau, Phei Li

doi:10.1007/s11356-024-32435-6

Cited by 7 publications

(3 citation statements)

References 78 publications

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“…In addition, most applications are still lab-based and performed in batch flow rather than continuous flow bioreactors (Onu et al, 2023;Rutland, 2023). Finally, ML models are interesting but are still under progress and the comparison of their performances is not yet sufficiently mature (Amran et al, 2024;Ling et al, 2024). Finally, in the context of this research, they do not provide single analytic expressions for the evolution of the methane production and simple methods for identifying the parameters characterizing the processes of methane production.…”

Section: Introductionmentioning

confidence: 99%

“…On the other side, because of the nonlinearity of the anaerobic digestion processes and the sensitivity to their parameters and operating conditions, the simple traditional empirical-driven models may not ensure efficient performance for a generalized prediction of biogas production (Amran et al, 2024;Rutland, 2023). Therefore, some models inspired from Artificial Intelligence and modern computation techniques have recently emerged as alternatives providing better performances for biogas estimation, prediction, and even for real-time control and monitoring of bioreactors (Ling et al, 2024;Swami et al, 2023). Some of these models are machinelearning-based requiring a large amount of data.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of some AM2 model parameters from a derived empirical logistic function of methane production

Zaatri

2024

EUR J SUSTAIN DEV RES

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Because of its capability to convert organic wastes into renewable energy and into some components useful for agriculture, the anaerobic digestion technology can reduce greenhouse gas emissions in the atmosphere and the pollution. Thus, anaerobic digestion can contribute to achieving some of sustainable development goals. Consequently, many theoretical and empirical approaches are proposed for estimating, predicting and optimizing the methane produced by anaerobic digestion. In this context, the logistic function is a mathematical model that can be used to approximate empirical data of the temporal methane production in anaerobic digestion. In a previous paper, under some appropriate approximations, we have derived from AM2 model a single analytical expression in a form of a logistic function for describing the evolution of methane production in batch bioreactors. In the present paper, by comparing the three standard parameters associated with the classical empirical logistic function with that of the derived one from AM2 model; some relationships between them have been established. These relations are exploited for estimating some coefficients and parameters of AM2 model with respect to empiric logistic function parameters and vice-versa. Moreover, this possibility enables more qualitative insight about the evolution of the methane production and the influence of AM2 parameters and coefficients as well as their interaction over its processes.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimation of some AM2 model parameters from a derived empirical logistic function of methane production

Zaatri

2024

EUR J SUSTAIN DEV RES

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“…To monitor and control industrial processes in different sectors, such as power generation systems, agriculture [1], and biogas [2], supervisory control and data acquisition (SCADA) systems have been developed. Explicitly in the case of photovoltaic (PV) systems, the SCADA systems can optimize PV system performance [3].…”

Section: Introductionmentioning

confidence: 99%

An Open-Source Supervisory Control and Data Acquisition Architecture for Photovoltaic System Monitoring Using ESP32, Banana Pi M4, and Node-RED

He,

Baig,

Iqbal

2024

Energies

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To overcome the issues of the existing properties and the non-configurable supervisory control and data acquisition (SCADA) architecture, this paper proposes an IoT-centered open-source SCADA system for monitoring photovoltaic (PV) systems. The system consists of three voltage sensors and three current sensors for data accumulation from the PV panel, the battery, and the load. As a part of the system design, a relay is used that controls the load remotely. An ESP32-E microcontroller transmits the collected data to a Banana Pi M4 Berry (BPI-M4 Berry) through the Message Queuing Telemetry Transport (MQTT) protocol over a privately established communication channel using Wi-Fi. The ESP32-E is configured as the MQTT publisher and the BPI-M4 Berry serves as the MQTT broker. Locally installed on the BPI-M4 Berry, the Node-RED platform creates highly customizable dashboards as human–machine interfaces (HMIs) to achieve real-time monitoring of the PV system. The proposed system was successfully tested to collect the PV system voltage/current/power data and to control the load in a supervisory way under a laboratory setup. The complete SCADA architecture details and test results for the PV system data during the total eclipse on 8 April 2024 and another day are presented in this paper.

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A comprehensive review on anaerobic digestion with focus on potential feedstocks, limitations associated and recent advances for biogas production

Dhull,

Kumar,

Yadav

et al. 2024

Environ Sci Pollut Res

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Machine learning methods for the modelling and optimisation of biogas production from anaerobic digestion: a review

Cited by 7 publications

References 78 publications

Estimation of some AM2 model parameters from a derived empirical logistic function of methane production

Estimation of some AM2 model parameters from a derived empirical logistic function of methane production

An Open-Source Supervisory Control and Data Acquisition Architecture for Photovoltaic System Monitoring Using ESP32, Banana Pi M4, and Node-RED

A comprehensive review on anaerobic digestion with focus on potential feedstocks, limitations associated and recent advances for biogas production

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