We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Artificial Neural Networks and Gradient Boosted Machines Used for Regression to Evaluate Gasification Processes: A Review
Summary
This review examined the use of artificial neural networks and gradient boosted machine learning models as regression tools for evaluating and optimizing gasification waste-to-energy processes, finding these approaches can effectively predict process performance across diverse gasification conditions.
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.
Sign in to start a discussion.
More Papers Like This
A Regression Analysis on Steam Gasification of Polyvinyl Chloride Waste for an Efficient and Environmentally Sustainable Process
Researchers investigated the use of machine learning regression models to predict and optimize steam gasification of polyvinyl chloride waste, finding that these models can effectively predict process performance and help identify optimal operating parameters.
Hydrogen production from plastic waste: A comprehensive simulation and machine learning study
Researchers used computer simulations and machine learning to optimize hydrogen production from polystyrene and polypropylene plastic waste through gasification. They found that increasing the gasification temperature up to 900 degrees Celsius significantly boosted hydrogen output, while higher pressures reduced production. The study demonstrates that converting plastic waste into hydrogen fuel could be an efficient way to address both energy needs and plastic pollution.
Evaluation and Prediction of Production Yields in Plastic Manufacturing Industry Using Artificial Neural Network
This study evaluated and predicted production yield in a plastic manufacturing company using artificial neural network modeling. Predictive tools that improve manufacturing efficiency can reduce material waste and off-specification plastic products that may contribute to environmental plastic pollution.
Neural network based prediction of the efficacy of ball milling to separate cable waste materials
Researchers developed a neural network — a type of artificial intelligence — to predict how well ball milling separates copper from plastic (PVC) in cable recycling, finding that the weight of cables loaded and the force of impact were the most critical factors. Machine learning tools like this could help scale up plastic and metal recycling to industrial levels.
Study on Thermal Degradation Processes of Polyethylene Terephthalate Microplastics Using the Kinetics and Artificial Neural Networks Models
Researchers studied the thermal degradation kinetics of PET microplastics using model-free and model-fitting methods alongside artificial neural networks, providing key parameters for optimizing pyrolysis-based recycling of plastic waste.