Browsing Chemical Engineering by Subject "machine learning"
Now showing items 1-8 of 8
-
Data Driven Modelling and Optimization of MEA Absorption Process for CO2 Capture
(University of Waterloo, 2020-05-27)Global warming is a rising issue and there are many research studies aiming to reduce greenhouse gas emissions. Carbon capture and storage technologies improved throughout the years to contribute as a solution to this ... -
Data-driven Optimization: Applications to Energy Infrastructure and Process Industry
(University of Waterloo, 2021-12-20)Nowadays, the existence and ease of access to massive amounts of data encourage proposing data-driven solutions. As optimization has always been based on the interchange between models and data, high-level optimization ... -
Deep deterministic policy gradient: applications in process control and integrated process design and control
(University of Waterloo, 2022-06-20)In recent years, the urgent need to develop sustainable processes to fight the negative effects of climate change has gained global attention and has led to the transition into renewable energies. As renewable sources ... -
Improved Slow Feature Analysis for Process Monitoring
(University of Waterloo, 2022-08-22)Unsupervised multivariate statistical analysis models are valuable tools for process monitoring and fault diagnosis. Among them, slow feature analysis (SFA) is widely studied and used due to its explicit statistical ... -
Incremental machine learning-based accelerator for computational fluid dynamics simulations
(University of Waterloo, 2023-10-16)The simulation of physicochemical processes with computational methods is key for engineering design, with applications in a variety of industries, ranging from pharmaceuticals to aerodynamics. Despite its importance and ... -
Machine Learning-Based Time Series Modelling with Applications for Forecasting Regional Wind Power and Air Quality Index
(University of Waterloo, 2021-08-27)Recently, time series forecasting has acquired a considerable academic and industrial interest in various areas for different applications. Machine learning (ML) algorithms are known for their ability to capture the chaotic ... -
Nonlinear model predictive control of a multiscale thin film deposition process using artificial neural networks
(Elsevier, 2019-11-02)The purpose of this study was to employ Artificial Neural Networks (ANNs) to develop data-driven models that would enable the shrinking horizon nonlinear model predictive control of a computationally intensive stochastic ... -
Predicting Adsorbent Performance for Carbon Capture using Machine Learning Models
(University of Waterloo, 2023-12-22)Carbon capture is a promising way to slow down climate change from anthropogenic sources. One of the carbon capture technologies that is being actively researched is adsorption. Given the increasing amount of literature ...