Imperial College London

DrCalvinTsay

Faculty of EngineeringDepartment of Computing

Lecturer in Computing
 
 
 
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Contact

 

+44 (0)20 7594 8241c.tsay Website

 
 
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Location

 

452Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
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39 results found

Folch JP, Lee RM, Shafei B, Walz D, Tsay C, van der Wilk M, Misener Ret al., 2023, Combining multi-fidelity modelling and asynchronous batch Bayesian Optimization, Computers and Chemical Engineering, Vol: 172, ISSN: 0098-1354

Bayesian Optimization is a useful tool for experiment design. Unfortunately, the classical, sequential setting of Bayesian Optimization does not translate well into laboratory experiments, for instance battery design, where measurements may come from different sources and their evaluations may require significant waiting times. Multi-fidelity Bayesian Optimization addresses the setting with measurements from different sources. Asynchronous batch Bayesian Optimization provides a framework to select new experiments before the results of the prior experiments are revealed. This paper proposes an algorithm combining multi-fidelity and asynchronous batch methods. We empirically study the algorithm behaviour, and show it can outperform single-fidelity batch methods and multi-fidelity sequential methods. As an application, we consider designing electrode materials for optimal performance in pouch cells using experiments with coin cells to approximate battery performance.

Journal article

Ceccon F, Jalving J, Haddad J, Thebelt A, Tsay C, Laird CD, Misener Ret al., 2022, OMLT: Optimization & Machine Learning Toolkit, Journal of Machine Learning Research, Vol: 23, ISSN: 1532-4435

The optimization and machine learning toolkit (OMLT) is an open-source software package incorporating neural network and gradient-boosted tree surrogate models, which have been trained using machine learning, into larger optimization problems. We discuss the advances in optimization technology that made OMLT possible and show how OMLT seamlessly integrates with the algebraic modeling language Pyomo. We demonstrate how to use OMLT for solving decision-making problems in both computer science and engineering.

Journal article

Thebelt A, Tsay C, Lee RM, Sudermann-Merx N, Walz D, Shafei B, Misener Ret al., 2022, Tree ensemble kernels for Bayesian optimization with known constraints over mixed-feature spaces

Tree ensembles can be well-suited for black-box optimization tasks such asalgorithm tuning and neural architecture search, as they achieve goodpredictive performance with little or no manual tuning, naturally handlediscrete feature spaces, and are relatively insensitive to outliers in thetraining data. Two well-known challenges in using tree ensembles for black-boxoptimization are (i) effectively quantifying model uncertainty for explorationand (ii) optimizing over the piece-wise constant acquisition function. Toaddress both points simultaneously, we propose using the kernel interpretationof tree ensembles as a Gaussian Process prior to obtain model varianceestimates, and we develop a compatible optimization formulation for theacquisition function. The latter further allows us to seamlessly integrateknown constraints to improve sampling efficiency by consideringdomain-knowledge in engineering settings and modeling search space symmetries,e.g., hierarchical relationships in neural architecture search. Our frameworkperforms as well as state-of-the-art methods for unconstrained black-boxoptimization over continuous/discrete features and outperforms competingmethods for problems combining mixed-variable feature spaces and known inputconstraints.

Journal article

Thebelt A, Wiebe J, Kronqvist JPF, Tsay C, Misener Ret al., 2022, Maximizing information from chemical engineering data sets: Applications to machine learning, Chemical Engineering Science, Vol: 252, Pages: 1-14, ISSN: 0009-2509

It is well-documented how artificial intelligence can have (and already is having) a big impact on chemical engineering. But classical machine learning approaches may be weak for many chemical engineering applications. This review discusses how challenging data characteristics arise in chemical engineering applications. We identify four characteristics of data arising in chemical engineering applications that make applying classical artificial intelligence approaches difficult: (1) high variance, low volume data, (2) low variance, high volume data, (3) noisy / corrupt / missing data, and (4) restricted data with physics-based limitations. For each of these four data characteristics, we discuss applications where these data characteristics arise and show how current chemical engineering research is extending the fields of data science and machine learning to incorporate these challenges. Finally, we identify several challenges for future research.

Journal article

Kelley MT, Tsay C, Cao Y, Wang Y, Flores-Cerrillo J, Baldea Met al., 2022, A data-driven linear formulation of the optimal demand response scheduling problem for an industrial air separation unit, CHEMICAL ENGINEERING SCIENCE, Vol: 252, ISSN: 0009-2509

Journal article

Kronqvist J, Misener R, Tsay C, 2022, P-split formulations: A class of intermediate formulations between big-M and convex hull for disjunctive constraints

We develop a class of mixed-integer formulations for disjunctive constraintsintermediate to the big-M and convex hull formulations in terms of relaxationstrength. The main idea is to capture the best of both the big-M and convexhull formulations: a computationally light formulation with a tight relaxation.The "$P$-split" formulations are based on a lifted transformation that splitsconvex additively separable constraints into $P$ partitions and forms theconvex hull of the linearized and partitioned disjunction. We analyze thecontinuous relaxation of the $P$-split formulations and show that, undercertain assumptions, the formulations form a hierarchy starting from a big-Mequivalent and converging to the convex hull. The goal of the $P$-splitformulations is to form a strong approximation of the convex hull through acomputationally simpler formulation. We computationally compare the $P$-splitformulations against big-M and convex hull formulations on 320 test instances.The test problems include K-means clustering, P_ball problems, and optimizationover trained ReLU neural networks. The computational results show promisingpotential of the $P$-split formulations. For many of the test problems,$P$-split formulations are solved with a similar number of explored nodes asthe convex hull formulation, while reducing the solution time by an order ofmagnitude and outperforming big-M both in time and number of explored nodes.

Journal article

Folch JP, Zhang S, Lee RM, Shafei B, Walz D, Tsay C, Wilk MVD, Misener Ret al., 2022, SnAKe: Bayesian Optimization with Pathwise Exploration

Bayesian Optimization is a very effective tool for optimizing expensiveblack-box functions. Inspired by applications developing and characterizingreaction chemistry using droplet microfluidic reactors, we consider a novelsetting where the expense of evaluating the function can increase significantlywhen making large input changes between iterations. We further assume we areworking asynchronously, meaning we have to select new queries before evaluatingprevious experiments. This paper investigates the problem and introduces'Sequential Bayesian Optimization via Adaptive Connecting Samples' (SnAKe),which provides a solution by considering large batches of queries andpreemptively building optimization paths that minimize input costs. Weinvestigate some convergence properties and empirically show that the algorithmis able to achieve regret similar to classical Bayesian Optimization algorithmsin both synchronous and asynchronous settings, while reducing input costssignificantly. We show the method is robust to the choice of its singlehyper-parameter and provide a parameter-free alternative.

Journal article

Thebelt A, Tsay C, Lee R, Sudermann-Merx N, Walz D, Tranter T, Misener Ret al., 2022, Multi-objective constrained optimization for energy applications via tree ensembles, Applied Energy, Vol: 306, Pages: 1-15, ISSN: 0306-2619

Energy systems optimization problems are complex due to strongly non-linear system behavior and multiple competing objectives, e.g. economic gain vs. environmental impact. Moreover, a large number of input variables and different variable types, e.g. continuous and categorical, are challenges commonly present in real-world applications. In some cases, proposed optimal solutions need to obey explicit input constraints related to physical properties or safety-critical operating conditions. This paper proposes a novel data-driven strategy using tree ensembles for constrained multi-objective optimization of black-box problems with heterogeneous variable spaces for which underlying system dynamics are either too complex to model or unknown. In an extensive case study comprised of synthetic benchmarks and relevant energy applications we demonstrate the competitive performance and sampling efficiency of the proposed algorithm compared to other state-of-the-art tools, making it a useful all-in-one solution for real-world applications with limited evaluation budgets.

Journal article

Cronjaeger C, Pattison RC, Tsay C, 2022, Tensor-Based Autoencoder Models for Hyperspectral Produce Data, Computer Aided Chemical Engineering, Pages: 1585-1590

Effectively monitoring and controlling product quality is critical in produce supply chain management. Hyperspectral imaging has emerged as a promising technique for monitoring food products, but the size of hyperspectral datasets complicates storage and processing. This work develops a novel architecture for autoencoder models that is well-suited for nonlinear subspace learning on tensorial, hyperspectral data. In particular, separate sub-models are used to (de)compress each mode of the data tensor, preserving spatial locality information and greatly reducing the number of autoencoder parameters. The approach enables memory-efficient training, nonlinear dimensionality reduction, and multi-task learning, as demonstrated by a real-world case study.

Book chapter

Ceccon F, Jalving J, Haddad J, Thebelt A, Tsay C, Laird CD, Misener Ret al., 2022, Presentation abstract: Optimization formulations for machine learning surrogates, Computer Aided Chemical Engineering, Pages: 57-58

In many process systems engineering applications, we seek to integrate surrogate models, e.g. already-trained neural network and gradient-boosted tree models, into larger decision-making problems. This presentation explores different ways to automatically take the machine learning surrogate model and produce an optimization formulation. Our goal is to automate the entire workflow of decision-making with surrogate models from input data to optimization formulation. This presentation discusses our progress towards this goal, gives examples of previous successes, and elicits a conversation with colleagues about the path forward.

Book chapter

Tsay C, 2021, Sobolev trained neural network surrogate models for optimization, Computers & Chemical Engineering, Vol: 153, Pages: 1-14, ISSN: 0098-1354

Neural network surrogate models are often used to replace complex mathematical models in black-box and grey-box optimization. This strategy essentially uses samples generated from a complex model to fit a data-driven, reduced-order model more amenable for optimization. Neural network models can be trained in Sobolev spaces, i.e., models are trained to match the complex function not only in terms of output values, but also the values of their derivatives to arbitrary degree. This paper examines the direct impacts of Sobolev training on neural network surrogate models embedded in optimization problems, and proposes a systematic strategy for scaling Sobolev-space targets during NN training. In particular, it is shown that Sobolev training results in surrogate models with more accurate derivatives (in addition to more accurately predicting outputs), with direct benefits in gradient-based optimization. Three case studies demonstrate the approach: black-box optimization of the Himmelblau function, and grey-box optimizations of a two-phase flash separator and two flashes in series. The results show that the advantages of Sobolev training are especially significant in cases of low data volume and/or optimal points near the boundary of the training dataset—areas where NN models traditionally struggle.

Journal article

Tsay C, Cao Y, Wang Y, Flores-Cerrillo J, Baldea Met al., 2021, Identification and Online Updating of Dynamic Models for Demand Response of an Industrial Air Separation Unit, 16th IFAC Symposium on Advanced Control of Chemical Processes (ADCHEM), Publisher: ELSEVIER, Pages: 140-145, ISSN: 2405-8963

Conference paper

Tsay C, Baldea M, 2021, Non-dimensional feature engineering and data-driven modeling for microchannel reactor control, IFAC 2020 World Congress, Publisher: Elsevier BV, Pages: 11295-11300, ISSN: 2405-8963

Catalytic plate microchannel reactors (CPRs) are a promising means for modular hydrogen/fuels production from distributed natural gas resources. However, the equipment miniaturization presents challenges for process control, including spatially-distributed models, limited availability of measurements, and fast process time constants. In the present paper, we investigate the use of data-driven models—specifically, artificial neural networks (ANNs)—to estimate temperature “hotspots” within CPRs. We prescribe nonlinear transformations of the model inputs in the form of well-known dimensionless quantities (e.g., Reynolds number), and we show that these engineered features can improve the prediction capability of computationally parsimonious ANNs using a first-principles reactor model. Finally, we present a simulation case study that demonstrates the use of a trained ANN for inferential model predictive control.

Conference paper

Seo K, Tsay C, Edgar TF, Stadtherr MA, Baldea Met al., 2021, Economic Optimization of Carbon Capture Processes Using Ionic Liquids: Toward Flexibility in Capture Rate and Feed Composition, ACS SUSTAINABLE CHEMISTRY & ENGINEERING, Vol: 9, Pages: 4823-4839, ISSN: 2168-0485

Journal article

Tsay C, Kronqvist J, Thebelt A, Misener Ret al., 2021, Partition-based formulations for mixed-integer optimization of trained ReLU neural networks, Publisher: arXiv

This paper introduces a class of mixed-integer formulations for trained ReLUneural networks. The approach balances model size and tightness by partitioningnode inputs into a number of groups and forming the convex hull over thepartitions via disjunctive programming. At one extreme, one partition per inputrecovers the convex hull of a node, i.e., the tightest possible formulation foreach node. For fewer partitions, we develop smaller relaxations thatapproximate the convex hull, and show that they outperform existingformulations. Specifically, we propose strategies for partitioning variablesbased on theoretical motivations and validate these strategies using extensivecomputational experiments. Furthermore, the proposed scheme complements knownalgorithmic approaches, e.g., optimization-based bound tightening capturesdependencies within a partition.

Conference paper

Kronqvist J, Misener R, Tsay C, 2021, Between steps: Intermediate relaxations between big-M and convex hull formulations, Publisher: arXiv

This work develops a class of relaxations in between the big-M and convexhull formulations of disjunctions, drawing advantages from both. The proposed"P-split" formulations split convex additively separable constraints into Ppartitions and form the convex hull of the partitioned disjuncts. Parameter Prepresents the trade-off of model size vs. relaxation strength. We examine thenovel formulations and prove that, under certain assumptions, the relaxationsform a hierarchy starting from a big-M equivalent and converging to the convexhull. We computationally compare the proposed formulations to big-M and convexhull formulations on a test set including: K-means clustering, P_ball problems,and ReLU neural networks. The computational results show that the intermediateP-split formulations can form strong outer approximations of the convex hullwith fewer variables and constraints than the extended convex hullformulations, giving significant computational advantages over both the big-Mand convex hull.

Conference paper

Seo K, Tsay C, Hong B, Edgar TF, Stadtherr MA, Baldea Met al., 2020, Rate-Based Process Optimization and Sensitivity Analysis for Ionic-Liquid-Based Post-Combustion Carbon Capture, ACS SUSTAINABLE CHEMISTRY & ENGINEERING, Vol: 8, Pages: 10242-10258, ISSN: 2168-0485

Journal article

Caspari A, Tsay C, Mhamdi A, Baldea M, Mitsos Aet al., 2020, The integration of scheduling and control: Top-down vs. bottom-up, JOURNAL OF PROCESS CONTROL, Vol: 91, Pages: 50-62, ISSN: 0959-1524

Journal article

Tsay C, Lejarza F, Stadtherr MA, Baldea Met al., 2020, Modeling, state estimation, and optimal control for the US COVID-19 outbreak, SCIENTIFIC REPORTS, Vol: 10, ISSN: 2045-2322

Journal article

Simkoff JM, Lejarza F, Kelley MT, Tsay C, Baldea Met al., 2020, Process Control and Energy Efficiency, ANNUAL REVIEW OF CHEMICAL AND BIOMOLECULAR ENGINEERING, VOL 11, Vol: 11, Pages: 423-445, ISSN: 1947-5438

Journal article

Tsay C, Baldea M, 2020, Integrating production scheduling and process control using latent variable dynamic models, CONTROL ENGINEERING PRACTICE, Vol: 94, ISSN: 0967-0661

Journal article

Tsay C, Pattison RC, Zhang Y, Rochelle GT, Baldea Met al., 2019, Rate-based modeling and economic optimization of next-generation amine-based carbon capture plants, Applied Energy, Vol: 252, Pages: 1-15, ISSN: 0306-2619

Amine scrubbing processes remain an important technology for mitigating the contribution of carbon emissions to global warming and climate change. Like other chemical processes, they can benefit from computer-aided optimization at the design stage, but systematic optimization procedures are rarely employed due to the challenges of simulating the requisite rate-based mass transfer and reaction models. This paper presents a novel approach for the simulation and optimization of rate-based columns, with specific application to the absorber and stripper columns found in (amine-) solvent-based carbon capture processes. The approach is based on pseudo-transient continuation, and the resulting column models are easily incorporated into large-scale process flowsheets with other previously developed pseudo-transient models. We demonstrate that the proposed approach allows for gradient-based optimization of next-generation amine scrubbing processes by considering a complex carbon capture process under three different operating conditions. The results provide general insight into the design of amine scrubbing processes, and shadow prices at the optimal point(s) suggest potential avenues for improving the process economics. The effects of carbon dioxide removal percentage and flue gas composition on process economics are briefly analyzed.

Journal article

Tsay C, Baldea M, 2019, 110th Anniversary: Using Data to Bridge the Time and Length Scales of Process Systems, INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, Vol: 58, Pages: 16696-16708, ISSN: 0888-5885

Journal article

Tsay C, Li Z, 2019, Automating Visual Inspection of Lyophilized Drug Products With Multi-Input Deep Neural Networks, 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE), Publisher: IEEE

Conference paper

Tsay C, Kumar A, Edgar T, Baldea Met al., 2019, Integrating Steady-State and Dynamic Models for Multi-Scale Flowsheet Optimization: A Steam-Methane Reforming Case Study, Foundations of Computer-Aided Process Design, Publisher: Elsevier, ISSN: 1570-7946

The bulk of hydrogen used in chemical and petrochemical processing is produced via steam-methane reforming (SMR), an energy-intensive process. SMR plant energy efficiency is often poorly predicted during simulation and optimization, owing to a lack of physical detail in the relevant unit operation models. Our recent work addressed this lack of detail by introducing a physics-based, distributed model of a steam-methane reforming furnace and incorporating the model into a multi-resolution hydrogen plant model. However, a sensitivity study found that efficiency at the optimal operating point is highly dependent on the performance of product separation, which relies on pressure-swing adsorption (PSA). As mathematical models of PSA units pose numerical challenges, previous studies of hydrogen production at the flowsheet level relied on simplified models, often ignoring dependence of the separation performance on the rest of the process. In this work, we integrate a dynamic PSA model into a multiresolution hydrogen plant flowsheet with a detailed SMR model. Further, we demonstrate that the resulting, large-scale model can be reliably simulated and optimized using pseudo-transient continuation.

Conference paper

Tsay C, Kumar A, Flores-Cerrillo J, Baldea Met al., 2019, Optimal demand response scheduling of an industrial air separation unit using data-driven dynamic models, COMPUTERS & CHEMICAL ENGINEERING, Vol: 126, Pages: 22-34, ISSN: 0098-1354

Journal article

Tsay C, Baldea M, 2019, Fast and efficient chemical process flowsheet simulation by pseudo-transient continuation on inertial manifolds, COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, Vol: 348, Pages: 935-953, ISSN: 0045-7825

Journal article

Tsay C, Kumar A, Edgar TF, Baldea Met al., 2019, INTEGRATING STEADY-STATE AND DYNAMIC MODELS FOR MULTI-SCALE FLOWSHEET OPTIMIZATION: A STEAM-METHANE REFORMING CASE STUDY, Editors: Munoz, Laird, Realff, Publisher: ELSEVIER SCIENCE BV, Pages: 403-408

Book chapter

Tsay C, Pattison RC, Baldea M, 2018, A pseudoÔÇÉtransient optimization framework for periodic processes: Pressure swing adsorption and simulated moving bed chromatography, AIChE Journal, Vol: 64, Pages: 2982-2996, ISSN: 0001-1541

Periodic systems are widely used in separation processes and in reaction engineering. They are designed for and operated at a cyclic steady state (CSS). Identifying and optimizing the CSS has proven to be computationally challenging. A novel framework for equation-oriented simulation and optimization of cyclic processes is introduced. A two-step reformulation of the process model is proposed, comprising, (1) a full discretization of the time and spatial domains and (2) recasting the discretized model as a differential-algebraic equation system, for which theoretical stability guarantees are provided. Additionally, a mathematical, structural connection between the CSS constraints and material recycling is established, which allows us to deal with these conditions via a “tearing” procedure. These developments are integrated in a pseudo-transient design optimization framework and two extensive case studies are presented: a simulated moving bed chromatography system and a pressure swing adsorption process. © 2017 American Institute of Chemical Engineers AIChE J, 64: 2982–2996, 2018

Journal article

Dias LS, Pattison RC, Tsay C, Baldea M, Ierapetritou MGet al., 2018, A simulation-based optimization framework for integrating scheduling and model predictive control, and its application to air separation units, COMPUTERS & CHEMICAL ENGINEERING, Vol: 113, Pages: 139-151, ISSN: 0098-1354

Journal article

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