Prediction of syngas production in the gasification process of biomass employing adaptive neuro-fuzzy inference system along with meta-heuristic algorithms
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Heng Wang
,
Shuming Cao
Abstract
Compared to fossil fuels, biomass fuels have minimal sulfur content, lower ash production, and significantly reduced emissions. The global need to reduce dependence on imported energy sources and preserve dwindling fossil fuel reserves underscores the importance of utilizing alternative energy resources. Biomass, with its abundant availability, presents a promising source for syngas production, even though the gasification procedure requires substantial energy due to its endothermic nature. Challenges related to the efficiency of biomass gasification and compliance with environmental standards have hindered economic viability. Much attention has been focused on predictive modeling of biomass gasification procedures to address these issues, necessitating robust frameworks capable of predicting parameters under varying operating conditions. This article introduces two hybrid frameworks, which are combined versions of Adaptive Neuro-Fuzzy Inference System (ANFIS) with Artificial Rabbits Optimization (ARO) and Crystal Structure Algorithm (CSA), based on proximate biomass values to predict elemental compositions (N2 and H2). These intelligent hybrid frameworks, trained with 70 % of biomass data, were further validated and tested with the remaining 15 % portions of the database. The frameworks were assessed based on some known performance metrics, namely, root mean squared error (RMSE), mean absolute error (MAE), coefficient of determination (R 2), RMSE-observations standard deviation ratio (RSR), and Nash-Sutcliffe Efficiency (NSE). Developed single and two hybrid frameworks compared and obtained outcomes revealed that both introduced optimizers efficiently promoted N2 and H2 estimation by ANFIS, especially CSA. R 2 values for ANCS were a maximum of 0.993 in both targets’ predictions. Also, minimum RMSE values of 1.007 and 1.470 related to N2 and H2 prediction emphasized the accuracy of ANCS, which is capable of being used in real-world applications.
Acknowledgments
We would like to take this opportunity to acknowledge that there are no individuals or organizations that require acknowledgment for their contributions to this work.
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Research ethics: Research involving Human Participants and Animals: The observational study conducted on medical staff needs no ethical code. Therefore, the above study was not required to acquire ethical code.
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Informed consent: This option is not necessary due to that the data were collected from the references.
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Author contributions: All authors contributed to the study’s conception and design. Data collection, simulation and analysis were performed by “Heng Wang, Shuming Cao and Xi Liu”. The first draft of the manuscript was written by “Heng Wang ”and all authors commented on previous versions of the manuscript. All authors have read and approved the manuscript.
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Use of Large Language Models, AI and Machine Learning Tools: During the preparation of this work, the authors used ChatGPT by OpenAI and Grammarly in order to assist with language refinement and ensure clarity and coherence in the manuscript, and perform grammar and spell checks. After using these tools/services, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article.
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Conflict of interests: The authors declare no competing interests.
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Research funding: No funding.
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Data availability: The authors do not have permission to share data.
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Supplementary Material
This article contains supplementary material (https://doi.org/10.1515/cppm-2024-0043).
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- Frontmatter
- Research Articles
- Comparative study of deterministic and stochastic optimization algorithms applied to the absorption of CO2 by alkanolamine solution
- Modeling the kinetics, energy consumption and shrinkage of avocado pear pulp during drying in a microwave assisted dryer
- Study of municipal solid waste treatment using plasma gasification by application of Aspen Plus
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- Industrial high saline water desalination by activated carbon in a packed column- an experimental and CFD study
- Dual-loop PID control strategy for ramp tracking and ramp disturbance handling for unstable CSTRs
- A control perspective on hybrid membrane/distillation propane/propylene separation process
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