Triple-objective optimization and electrochemical/technical/environmental study of biomass gasification process for a novel high-temperature fuel cell/electrolyzer/desalination scheme

The escalating trends in energy consumption and the associated emissions of pollutants in the past century have led to energy depletion and environmental pollution. Achieving comprehensive sustainability requires the optimization of energy efficiency and the implementation of efficient energy management strategies. Artificial intelligence (AI), a prominent machine learning paradigm, has gained significant traction in control applications and found extensive utility in various energy-related domains. The utilization of AI techniques for addressing energy-related challenges is favored due to their aptitude for handling complex and nonlinear data structures. Based on the preliminary inquiries, it has been observed that predictive analytics, prominently driven by artificial neural network (ANN) algorithms, assumes a crucial position in energy management across various sectors. This paper presents a comprehensive bibliometric analysis to gain deeper insights into the progression of AI in energy research from 2003 to 2023. AI models can be used to accurately predict energy consumption, load profiles, and resource planning, ensuring consistent performance and efficient resource utilization. This review article summarizes the existing literature on the implementation of AI in the development of energy management systems. Additionally, it explores the challenges and potential areas of research in applying ANN to energy system management. The study demonstrates that ANN can effectively address integration issues between energy and power systems, such as solar and wind forecasting, power system frequency analysis and control, and transient stability assessment. Based on the comprehensive state-of-the-art study, it can be inferred that the implementation of AI has consistently led to energy reductions exceeding 25%. Furthermore, this article discusses future research directions in this field.

Section: Review Articlementioning

confidence: 99%

Harnessing artificial intelligence for data-driven energy predictive analytics: A systematic survey towards enhancing sustainability

Le,

Priya,

et al. 2024

“…The combined heat and power (CHP) system also employs syngas from biomass gasification as an appropriate substitute for fossil fuel (J. Thi et al, 2024;Zhang et al, 2022). The byproduct of biomass gasification, a porous organic residue known as biochar finds use in soil carbon sequestration, pollution absorption, pollution remediation, and the synthesis of carbon-based products (Fiore et al, 2020;Gonçalves et al, 2019;Hoang et al, 2022c;Richardson et al, 2015).…”

Section: Application Of Machine Learning In Biomassmentioning

confidence: 99%

Unlocking renewable energy potential: Harnessing machine learning and intelligent algorithms

Le,

Paramasivam,

Adril

et al. 2024

This review article examines the revolutionary possibilities of machine learning (ML) and intelligent algorithms for enabling renewable energy, with an emphasis on the energy domains of solar, wind, biofuel, and biomass. Critical problems such as data variability, system inefficiencies, and predictive maintenance are addressed by the integration of ML in renewable energy systems. Machine learning improves solar irradiance prediction accuracy and maximizes photovoltaic system performance in the solar energy sector. ML algorithms help to generate electricity more reliably by enhancing wind speed forecasts and wind turbine efficiency. ML improves the efficiency of biofuel production by optimizing feedstock selection, process parameters, and yield forecasts. Similarly, ML models in biomass energy provide effective thermal conversion procedures and real-time process management, guaranteeing increased energy production and operational stability. Even with the enormous advantages, problems such as data quality, interpretability of the models, computing requirements, and integration with current systems still remain. Resolving these issues calls for interdisciplinary cooperation, developments in computer technology, and encouraging legislative frameworks. This study emphasizes the vital role of ML in promoting sustainable and efficient renewable energy systems by giving a thorough review of present ML applications in renewable energy, highlighting continuing problems, and outlining future prospects

“…Solid waste gasification is based on the principle of fuel combustion in an oxygen-deficient environment to produce syngas consisting of the main components CO, H2, CH4, CO2 and N2 (Jamro et al, 2022;Tang et al, 2022;Zuo et al, 2022). Regarding the application of syngas from biomass or solid waste gasification, recent studies show that syngas could be used for power generation (Ali et al, 2023;Shahavi et al, 2022;Zhang et al, 2022). The above reviews show that converting solid waste into RDF pellets to produce syngas for power generation engines is a worldwide trend.…”

Section: Introductionmentioning

confidence: 99%

Simulation and experimental study of refuse-derived fuel gasification in an updraft gasifier

Nguyen-Thi

Bui

2023

Refuse-derived fuel (RDF) made from the mixture of wood and loose rice husk increases the porosity of the fuel in the furnace to facilitate the gasification process. Simulation results show that CO is concentrated in the incomplete combustion zone and CO2 forms mainly in the fully burned area; CH4 forms in the reduction region, while H2 forms in the region of high temperature of the furnace. When the mixture composition was f=0.3, the CO concentration in the syngas reached about 21%, the H2 concentration reached about 2% and the CH4 concentration was too low to be ignored. When the mixture composition increased to f = 0.5, the CO concentration reached about 26%, the H2 concentration remained almost unchanged and the CH4 content increased to 6%. The calorific value of the syngas reached a maximum when f = 0.5 and the temperature of the reduction zone is in the range of 900K to 1200K. Air humidity affects CO concentration but not much on CH4 and H2 concentration as well as the syngas calorific value. The difference between simulation and experimental results is not more than 10% for CH4 concentration and not more than 14% for CO2 concentration. The power of the spark ignition engine is reduced by 30% when running on syngas compared to when running on gasoline.