Rapidly increasing solid waste generation and energy demand are two critical issues of the current century. Plasma gasification, a type of waste-to-energy (WtE) technology, has the potential to produce clean energy from waste and safely destroy hazardous waste. Among plasma gasification technologies, microwave (MW)-driven plasma offers numerous potential advantages to be scaled as a leading WtE technology if its processes are well understood and optimized. This paper reviews studies on modeling experimental microwave-induced plasma gasification systems. The system characterization requires developing mathematical models to describe the multiphysics phenomena within the reactor. The injection of plasma-forming gases and carrier gases, the rate of the waste stream, and the operational power heavily influence the initiation of various chemical reactions that produce syngas. The type and kinetics of the chemical reactions taking place are primarily influenced by either the turbulence or temperature. Navier–Stokes equations are used to describe the mass, momentum, and energy transfer, and the k-epsilon model is often used to describe the turbulence within the reactor. Computational fluid dynamics software offers the ability to solve these multiphysics mathematical models efficiently and accurately.
Waste-to-Energy technologies have the potential to dramatically improve both the natural and human environment. One type of waste-to-energy technology that has been successful is gasification. There are numerous types of gasification processes and in order to drive understanding and the optimization of these systems, traditional approaches like computational fluid dynamics software have been utilized to model these systems. The modern advent of machine learning models has allowed for accurate and computationally efficient predictions for gasification systems that are informed by numerous experimental and numerical solutions. Two types of machine learning models that have been widely used to solve for quantitative variables that are of predictive interest in gasification systems are gradient boosted machines and artificial neural networks. In this article, the reviewed literature used either gradient boosted machines or artificial neural networks to successfully model gasification systems. The review of such literature allows for a comparison in machine learning model architecture and resultant accuracy as well as an insight into what parameters are being used to inform the models and to make predictions.
Microwave-driven plasma gasification technology has the potential to produce clean energy from municipal and industrial solid wastes. It can generate temperatures above 2000 K (as high as 30,000 K) in a reactor, leading to complete combustion and reduction of toxic byproducts. Characterizing complex processes inside such a system is however challenging. In previous studies, simulations using computational fluid dynamics (CFD) produced reproducible results, but the simulations are tedious and involve assumptions. In this study, we propose machine-learning models that can be used in tandem with CFD, to accelerate high-fidelity fluid simulation, improve turbulence modeling, and enhance reduced-order models. A two-dimensional microwave-driven plasma gasification reactor was developed in ANSYS (Ansys, Canonsburg, PA, USA) Fluent (a CFD tool), to create 644 (geometry and temperature) datasets for training six machine-learning (ML) models. When fed with just geometry datasets, these ML models were able to predict the proportion of the reactor area with temperature above 2000 K. This temperature level is considered a benchmark to prevent formation of undesirable byproducts. The ML model that achieved highest prediction accuracy was the feed forward neural network; the mean absolute error was 0.011. This novel machine-learning model can enable future optimization of experimental microwave plasma gasification systems for application in waste-to-energy.
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