ABSTRACT
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.
ABSTRACT
The recent COVID-19 pandemic, which has hit the world, is third in the last two decades. The safety and precaution measures have led to the generation of a colossal pile of biomedical waste, including plastic waste, due to the usage of personal protective equipment kits and safety equipment that is not easily manageable. The environment and health and safety concerns for humans require biomedical waste to be treated with an outstanding treatment process that can help humanity manage it by adhering to strict environmental norms prescribed. The plasma gasification technology is the most beneficial and efficient technology for treating biomedical waste. The byproducts generated can be utilized further as valuable inputs in other industries, thus strengthening the circular economy concept. In this research paper, the applicability of plasma gasification for the treatment of biomedical waste in the present scenario has been reviewed. The feasibility and applicability of the technology in handling biomedical waste have been reviewed via various research articles in this study. Also, further steps have been suggested for the Indian scenario to make this technology commercially viable in the long run.
ABSTRACT
In terms of infection control in hospitals, especially the Covid-19 pandemic that we are living in, it has revealed the necessity of proper disposal of medical waste. The increasing amount of medical waste with the pandemic is straining the capacity of incineration facilities or storage areas. Converting this waste to energy with gasification technologies instead of incineration is also important for sustainability. This study investigates the gasification characteristics of the medical waste in a novel updraft plasma gasifier with numerical simulations in the presence of the plasma reactions. Three different medical waste samples, chosen according to the carbon content and five different equivalence ratios (ER) ranging from 0.1 to 0.5 are considered in the simulations to compare the effects of different chemical compositions and waste feeding rates on hydrogen (H2) content and syngas production. The outlet properties of a 10 kW microwave air plasma generator are used to define the plasma inlet in the numerical model and the air flow rate is held constant for all cases. Results showed that the maximum H2 production can be obtained with ER = 0.1 for all waste samples.