Process modelling and optimisation of methane yield from palm oil mill effluent using response surface methodology and artificial neural network-Reference-Cited by-同舟云学术

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Process modelling and optimisation of methane yield from palm oil mill effluent using response surface methodology and artificial neural network

Published:2023-04 Issue: Volume:52 Page:103493
ISSN:2214-7144
Container-title:Journal of Water Process Engineering
language:en
Short-container-title:Journal of Water Process Engineering

Author:

Chen Jia Win,Chan Yi Jing,Arumugasamy Senthil Kumar,Yazdi Sara Kazemi

Publisher

Elsevier BV

Subject

Process Chemistry and Technology,Waste Management and Disposal,Safety, Risk, Reliability and Quality,Biotechnology

Reference47 articles.

1. A study of palm oil mill processing and environmental assessment of palm oil mill effluent treatment;Akhbari;Environ. Eng. Res.,2020

2. ‘Palm Oil Mill Effluent as an Environmental Pollutant’, Palm Oil [Preprint];Kamyab,2018

3. Palm oil mill effluent treatment processes—A review;Mohammad;Processes,2021

4. Development of anaerobic digestion methods for palm oil mill effluent (POME) treatment;Poh;Bioresour. Technol.,2009

Cited by 5 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Enhanced predictive optimization of methane dry reforming via ResponseSurface methodology and artificial neural network approaches: Insights using a novel nickel-strontium-zirconium-aluminum catalyst;Molecular Catalysis;2024-06

2. Machine learning methods for the modelling and optimisation of biogas production from anaerobic digestion: a review;Environmental Science and Pollution Research;2024-02-20

3. Industrial-scale anaerobic Co-digestion (ACoD) of palm oil mill effluent (POME) and decanter cake (DC) for maximizing methane yield: An integrated machine learning and simulation-based economic analysis approach;Energy;2024-02

4. A Systematic Review of Machine-Learning Solutions in Anaerobic Digestion;Bioengineering;2023-12-11

5. Derivation of Optimal Operation Factors of Anaerobic Digesters through Artificial Neural Network Technology;Systems;2023-07-22

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