Optimization and control of a thin film growth process: A hybrid first principles/artificial neural network based multiscale modelling approach

The final publication is available at Elsevier via https://dx.doi.org/10.1016/j.compchemeng.2018.08.029 © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/This work details the construction and evaluation of a low computational cost hybrid multiscale thin film deposition model that couples artificial neural networks (ANNs) with a mechanistic (first-principles) multiscale model. The multiscale model combines continuum differential equations, which describe the transport of the precursor gas phase, with a stochastic partial differential equation (SPDE) that predicts the evolution of the thin film surface. In order to allow the SPDE to accurately predict the thin film growth over a range of system parameters, an ANN is developed and trained to predict the values of the SPDE coefficients. The fully-assembled hybrid multiscale model is validated through comparison against a kinetic Monte Carlo-based thin film multiscale model. The model is subsequently applied to a series of optimization and control studies to test its performance under different scenarios. These studies illustrate the computational efficiency of the proposed hybrid multiscale model for optimization and control applications.Natural Sciences and Engineering Research Council of Canad

Multilevel Monte Carlo for noise estimation in stochastic multiscale systems

The final publication is available at Elsevier via https://doi.org/10.1016/j.cherd.2018.10.006� 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/The purpose of this study is to adapt Multilevel Monte Carlo (MLMC) sampling technique for random noise estimation in stochastic multiscale systems and evaluate the performance of this method. The system under consideration was a simulation of thin film formation by chemical vapour deposition, where a kinetic Monte Carlo solid-on-solid model was coupled with partial differential equations that represented mass, energy and momentum transport. The noise in the expected value of the system�s observable (film roughness) was estimated using MLMC and standard Monte Carlo (MC) sampling. The MLMC technique achieved conservative estimates of noise in the observable at an order of magnitude lower computational cost than standard MC sampling. This study highlights the nuances of adapting the MLMC technique to the stochastic multiscale system and provides insight on the benefits and challenges of using MLMC for noise estimation in stochastic multiscale systems.Natural Sciences and Engineering Research Council of Canad

A dynamic optimization framework for integration of design, control and scheduling of multi-product chemical processes under disturbance and uncertainty

The final publication is available at Elsevier via http://dx.doi.org/10.1016/j.compchemeng.2017.05.007 © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/A novel dynamic optimization framework is presented for integration of design, control, and scheduling for multi-product processes in the presence of disturbances and parameter uncertainty. This framework proposes an iterative algorithm that decomposes the overall problem into flexibility and feasibility analyses. The flexibility problem is solved under a critical (worst-case) set of disturbance and uncertainty realizations, whereas the feasibility problem evaluates the dynamic feasibility of each realization, and updates the critical set accordingly. The algorithm terminates when a robust solution is found, which is feasible under all identified scenarios. To account for the importance of grade transitions in multiproduct processes, the proposed framework integrates scheduling into the dynamic model by the use of flexible finite elements. This framework is applied to a multi-product continuous stirred-tank reactor (CSTR) system subject to disturbance and parameter uncertainty. The proposed method is shown to return robust solutions that are of higher quality than the traditional sequential method. The results indicate that scheduling decisions are affected by design and control decisions, thus motivating the need for integration of these three aspects.Natural Sciences & Engineering Council of Canada (NSERC)Ontario Graduate Scholarship (OGS

Nonlinear model predictive control of a multiscale thin film deposition process using artificial neural networks

The final publication is available at Elsevier via https://doi.org/10.1016/j.ces.2019.07.044. © 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/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 multiscale system. The system of choice was a simulation of thin film formation by chemical vapour deposition. Two ANNs were trained to estimate the system’s observables. The ANNs were subsequently employed in a shrinking horizon optimization scheme to obtain the optimal time-varying profiles of the manipulated variables that would meet the desired thin film properties at the end of the batch. The resulting profiles were validated using the stochastic multiscale system and a good agreement with the predictions of the ANNs was observed. Due to their observed computational efficiency, accuracy, and the ability to reject disturbances, the ANNs seem to be a promising approach for online optimization and control of computationally demanding multiscale process systems.The authors would like to gratefully acknowledge the Natural Sciences and Engineering Research Council of Canada (NSERC) for the financial support

Centralized and hierarchical scheduling frameworks for copper smelting process

Optimal scheduling of copper smelting process is an ongoing challenge due to conflicting objectives of the various process units and the inter-dependencies that exist among these units. To design a scheduling framework, two potential alternatives – centralized and hierarchical approaches – can address those inter-dependencies in this process. These approaches represent the two extremes and the choice depends on the accuracy, reliability, and complexity of the scheduling task. In this study, optimization-based centralized and hierarchical scheduling frameworks are developed to find an optimal schedule for the smelting process, considering the inter-dependencies among process units. We propose a practical and effective coordination scheme for the hierarchical framework that finds a near-optimal schedule with reasonable computational demands. Two case studies are presented to demonstrate that the proposed hierarchical framework is capable of finding a near plant-wide optimum for the copper smelting process and it can be used in similar plant-wide scheduling applications.publishedVersionPeer reviewe

Modelling and optimization of a pilot-scale entrained-flow gasifier using artificial neural networks

The final publication is available at Elsevier via https://doi.org/10.1016/j.energy.2019.116076. © 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/This paper explores the construction and validation of an artificial neural network (ANN) in order to accurately and efficiently predict the performance of a pilot-scale gasifier unit. This ANN model consists of multiple sub-networks that individually predict each of the desired gasifier outputs as a function of key system parameters. The ANN was trained using data generated for a large set of randomly-generated input conditions from a pilot-scale gasifier reduced order model (ROM) developed previously. The fully-trained ANN was validated by comparing its performance to the aforementioned ROM model. The validated ANN model was subsequently implemented into two optimization studies in order to determine the operating conditions necessary to maximize the carbon conversion under different limitations for the peak temperature of the gasifier and to determine the ideal input conditions of maximizing both the carbon conversion and production of hydrogen gas which are two conflicting objectives. This case study further showcases the benefit of the ANN, which was able to obtain accurate predictions for the gasifier results similar to the results generated by the ROM model at a much lower computational cost.Natural Sciences and Engineering Research Counci

Discrete-Time Network Scheduling and Dynamic Optimization of Batch Processes with Variable Processing Times through Discrete-Steepest Descent Optimization

This work proposes a general discrete-time simultaneous scheduling and dynamic optimization (SSDO) formulation based on the state-task network (STN) representation. This formulation explicitly considers variable processing times, which is a key aspect in the integration of scheduling and control decisions. The resulting Mixed-Integer Nonlinear Programming (MINLP) problem is solved using a custom Discrete-Steepest Descent Algorithm (D-SDA), which is designed to efficiently explore the ordered discrete decisions in the formulation, i.e., processing times and batching variables. The performance of the proposed solution framework is illustrated using two case studies adapted from the literature. The results show that the D-SDA explores the feasible region of ordered discrete decisions more efficiently than a general-purpose MINLP solver, leading to more profitable solutions in shorter computational times