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Sparse Identification Modeling and Predictive Control of Nonlinear Processes
No full textData is widely recognized as a crucial player in the fourth industrial revolution, in which engineers and computers must harness data to enhance the efficiency of industrial processes and their associated control systems. Traditional industrial process control systems rely on linear data-driven models, with parameters fitted to experimental or simulated data. In specific control loops, such as those critical for profit optimization, they may employ first-principles models describing the underlying physico-chemical phenomena but with a few data-derived parameters. Nevertheless, modeling complex, nonlinear processes on a large scale remains an open challenge in process systems engineering. The quality of these models depends on various factors, including model parameter estimation, model uncertainty, the number of assumptions made during model development, model dimensionality, structure, and the computational demands for real-time model solutions [1,2]. This is especially pertinent as process models are integral to advanced model-based control systems, such as model predictive control (MPC) and economic MPC (EMPC). Designing MPC systems that utilize data-driven modeling techniques to account in real-time for large data sets is a new frontier that will impact the next generation of industrial control systems. While a significant body of research has been dedicated to the use of neural networks for nonlinear process modeling and control, in both the theoretical [3] and practical [4] domains, more computationally efficient models that can directly be used in MPC rather than their linearized counterparts, are still an growing area of research that can lead to the design of more robust and efficient control systems.Motivated by the above considerations, this dissertation presents the use of a computationally efficient data-driven technique known as sparse identification in model predictive control for chemical processes described by nonlinear dynamic models. The motivation and organization of this dissertation are first presented. Then, the use of sparse identification to develop nonlinear dynamic process models to be used in model predictive controllers is presented, specifically addressing the challenges of two-time-scale systems, sensor noise, industrial nonlinearities, and process shifts. The MPC and economic MPC schemes that use sparse identified models are presented in detail with rigorous analysis provided on their closed-loop stability and recursive feasibility properties. Finally, the dissertation closes with an overview of the novelties introduced to overcome the aforementioned challenges and a detailed guide to developing nonlinear process models for complex chemical processes using sparse identification. Throughout the dissertation, the proposed methods are applied to numerical simulations of nonlinear chemical process examples and Aspen Plus simulations of large-scale chemical process networks to demonstrate their effectiveness.[1] S. S. Ge and C.Wang. Adaptive neural control of uncertain MIMO nonlinear systems. IEEE Transactions on Neural Networks, 15:674–692, 2004.[2] H. W. Ge, Y. C. Liang, and M. Marchese. A modified particle swarm optimization-based dynamic recurrent neural network for identifying and controlling nonlinear systems. Computers & Structures, 85:1611–1622, 2007.
[3] Z. Wu, A. Tran, D. Rincon, and P. D. Christofides. Machine learning-based predictive control of nonlinear processes. Part I: Theory. AIChE Journal, 65:e16729, 2019.
[4] J. Luo, B. Çıtmacı, J. B. Jang, F. Abdullah, C. G. Morales-Guio, and P. D. Christofides. Machine learning-based predictive control using on-line model linearization: Application to an experimental electrochemical reactor. Chemical Engineering Research and Design, 197:721–737, 2023
Graph Guessing Games and Non-Shannon Information Inequalities
No full textGuessing games for directed graphs were introduced by Riis [12] for studying multiple unicast network coding problems. In a guessing game, the players toss generalised dice and can see some of the other outcomes depending on the structure of an underlying digraph. They later guess simultaneously the outcome of their own die. Their objective is to find a strategy which maximises the probability that they all guess correctly. The performance of the optimal strategy for a graph is measured by the guessing number of the digraph.
In [3], Christofides and Markstrom studied guessing numbers of undirected graphs and defined a strategy which they conjectured to be optimal. One of the main results of this paper is a disproof of this conjecture.
The main tool so far for computing guessing numbers of graphs is information theoretic inequalities. The other main result of the paper is that Shannon's information inequalities, which work particularly well for a wide range of graph classes, are not sufficient for computing the guessing number.
Finally we pose a few more interesting questions some of which we can answer and some which we leave as open problems
Hybrid Christofides Algorithm and List-Based Simulated Annealing (HCA-LBSA) for Solving Traveling Salesman Problem (TSP)
No full textThe Traveling Salesman Problem (TSP) is a well-known classical problem in combinatorial optimization and graph theory. This study proposes the Hybrid Christofides Algorithm and List-Based Simulated Annealing (HCA-LBSA) to solve TSP. This approach employs the Christofides algorithm as an initial solution due to its guarantee that the result does not exceed 3/2 of the optimal solution. To further enhance solution quality, optimization is performed using List-Based Simulated Annealing (LBSA). LBSA is an adaptive version of Simulated Annealing (SA), integrating a temperature list, Variable Markov Chain Length (VMCL), and the Heuristic Augmented Instance-Based Sampling Method to improve the efficiency of optimal solution exploration. The evaluation was conducted on 32 datasets, where HCA-LBSA achieved an average percentage error of the average tour length (PEav) of 0.619% with an average execution time of 23.285 seconds. From the parameter tuning process, the optimal parameter combination for HCA-LBSA is Lmax (the length of the temperature list) of 140 and pos (the relative position of the generation with the maximum MCL) of 0.5. The performance of HCA-LBSA is also compared with the ELBSA, LBSA, ASA-GS, SOS-SA, AHSA-TS, and D-CLPSO algorithms. Experimental results indicate that HCA-LBSA significantly outperforms LBSA, ASA-GS, AHSA-TS, and D-CLPSO. This demonstrates that HCA LBSA is capable of providing effective solutions for solving the TSP.62 PagesSkripsi Sarjan
MeSH term explosion and author rank improve expert recommendations
No full textInformation overload is an often-cited phenomenon that reduces the productivity, efficiency and efficacy of scientists. One challenge for scientists is to find appropriate collaborators in their research. The literature describes various solutions to the problem of expertise location, but most current approaches do not appear to be very suitable for expert recommendations in biomedical research. In this study, we present the development and initial evaluation of a vector space model-based algorithm to calculate researcher similarity using four inputs: 1) MeSH terms of publications; 2) MeSH terms and author rank; 3) exploded MeSH terms; and 4) exploded MeSH terms and author rank. We developed and evaluated the algorithm using a data set of 17,525 authors and their 22,542 papers. On average, our algorithms correctly predicted 2.5 of the top 5/10 coauthors of individual scientists. Exploded MeSH and author rank outperformed all other algorithms in accuracy, followed closely by MeSH and author rank. Our results show that the accuracy of MeSH term-based matching can be enhanced with other metadata such as author rank
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
"Closing the R&D Gap, Evaluating the Sources of R&D Spending"
No full textBoth spending and tax policies have been implemented in the United States with the goal of stimulating private sector research and development (R&D). Karier questions whether current R&D policy, especially the research and experimentation tax credit, can contribute to closing the gap between nondefense expenditures on R&D in the United States and such expenditures in other countries, such as Japan and Germany. He also explores possible changes to our current R&D policy to make it more effective.
What is Othello’s Secret?
No full textExplicitly written from the perspective of a second-generation British Cypriot, this article examines the relevance of Shakespeare’s Othello to the modern troubles of Cyprus. Drawing on the recurrent imperialist and nationalist struggles to control Cyprus, in Shakespeare’s day and our own, the article explains how the author’s upcoming book, Othello’s Secret: The Cyprus Problem, radically reinterprets the domestic and military tensions of Othello as precursors to the island’s more recent wars and divisions. Insight into the way an English writer in the early modern period understood Cyprus can contribute to the way scholars in the British academy understand the bard both in his context and in ours. Consequently, the article challenges the conventional Anglophone scholarly focus on Venice, highlighting a surprising academic blindspot given Britain’s historical and ongoing colonial presence on Cyprus. In so doing, it reframes Othello as a play about Cyprus, offering a more personal account of how research on Shakespeare can purposefully contribute to geopolitical debates
Improved postprandial glucose control with a customized model predictive controller
No full textMeal compensation in blood glucose control of people affected by type 1 Diabetes is an open challenge. The proposed Model Predictive Controller (MPC) is equipped with an asymmetric quadratic cost function, postprandial (pp) input integral and pp output soft constraints. The controller is synthesized with a linear glucose-insulin model customized on the basis of the patient clinical knowledge. An in-silico study on 100 adult virtual patients of the UVA/Padova simulator is performed to evaluate the achieved controller performance. This is compared with the performance obtained with a previously developed MPC. The evaluation is performed in perturbed scenario in which the controller is not aware of random variations of insulin sensitivity in each virtual patient. The proposed controller is shown to be able to significantly increase the average control performance and to reduce both hyper- and hypoglycemia phenomena. A nonlinear version of the proposed MPC, whose performance is evaluated in a nominal scenario, is also considered as ideal reference
Dispelling the Myths Behind First-author Citation Counts
No full textWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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