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intelligent systems in process engineering: A review

COMPUTERS & CHEMICAL engineering 1996年第6-7期20卷 743-791页

作者： Stephanopoulos, G Han, C Laboratory for Intelligent Systems in Process Engineering Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA 02139 U.S.A.

The purpose of this review is three-fold. First, sketch the directions that research and industrial applications of ''intelligent systems'' have taken in several areas of process engineering. Second, identify the emerging trends in each area, as well as the common threads that cut across several domains of inquiry. Third, stipulate research and development themes of significant importance for the future evolution of ''intelligent systems'' in process engineering. The paper covers the following seven areas: diagnosis of process operations;monitoring and analysis of process trends;intelligent control;heuristics and logic in planning and scheduling of process operations;modeling languages, simulation, and reasoning;intelligence in scientific computing;knowledge-based engineering design. Certain trends seem to be common and will (in all likelihood) characterize the nature of the future deployment of ''intelligent systems''. These trends are: (1) Specialization to narrowly defined classes of problems. (2) Integration of multiple knowledge representations, so that all of relevant knowledge is captured and utilized. (3) Integration of processing methodologies, which tends to blur the past sharp distinctions between AI-based techniques and those from operations research, systems and control theory, probability and statistics. (4) Rapidly expanding range of industrial applications with significant increase in the scope of engineering tasks and size of problems.

关键词： Chemical engineering

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Stealthy Attacks on Sampled-Data Control and Encryption-Based Countermeasures

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IEEE Transactions on Automatic Control 2025年

作者： Shang, Jun Li, Yuzhe Chen, Tongwen Tongji University Department of Control Science and Engineering Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai200092 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110004 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada

This paper investigates stealthy attacks on sampled-data control systems, where a continuous process is sampled periodically, and the resultant discrete output and control signals are transmitted through dual channels in wireless communication networks. Acknowledging the vulnerability of these transmitted data to cyber threats, we analyze two types of stealthy attacks that compromise either one or both channels. We explicitly present the necessary and sufficient conditions for the existence of stealthy single-channel attacks. To mitigate these security risks, we implement encryption-based countermeasures, including linear time-invariant and periodic encryption methods. We detail sufficient conditions under which these encryption strategies effectively thwart single- and dual-channel stealthy attacks. The effectiveness of these approaches and the theoretical findings are demonstrated through numerical simulations. © 1963-2012 IEEE.

关键词： Cyber attacks

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Edible aquatic robots with Marangoni propulsion

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Nature Communications 2025年第1期16卷 1-9页

作者： Zhang, Shuhang Kwak, Bokeon Zhu, Ruihao Pankhurst, Markéta Zhang, Lu Boom, Remko M. Floreano, Dario Laboratory of Intelligent Systems School of Engineering École Polytechnique Fédérale de Lausanne STI-IGM-LIS Lausanne Switzerland Laboratory of Food Process Engineering Wageningen University and Research Wageningen Netherlands

Centimeter-scale aquatic robots could be used in environmental monitoring, exploration, and intervention in aquatic environments. However, existing robots rely on artificial polymers and commercial electronic components, which can pollute and disrupt sensitive ecological environments if they are not retrieved. To address these challenges, we describe a fully biodegradable and fully edible self-propelled device that leverages the Marangoni effect for autonomous propulsion. The body of the edible aquatic robot is made of freeze-dried fish food and is powered by a water-triggered pneumatic reaction that produces motion by sustained release of a surfactant that is safe for aquatic fauna. The device’s biodegradable and non-toxic composition allows for safe environmental deployment for environmental sensing, delivery of nutrition or medication in aquatic environments. The proposed method substantially expands the potential benefits of small-scale aquatic robots that could be deployed on a large scale without the need to retrieve them and even provide nutrition to wildlife at the end of their lifetime as animals do. © The Author(s) 2025.

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Distributed Online Convex Optimization with Time-Varying Constraints: Tighter Cumulative Constraint Violation Bounds under Slater's Condition

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IEEE Transactions on Automatic Control 2025年

作者： Yi, Xinlei Li, Xiuxian Yang, Tao Xie, Lihua Hong, Yiguang Chai, Tianyou Johansson, Karl H. National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai Institute of Intelligent Science and Technology China Ministry of Education Frontiers Science Center for Intelligent Autonomous Systems China Massachusetts Institute of Technology Lab for Information & Decision Systems CambridgeMA02139 United States Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Nanyang Technological University School of Electrical and Electronic Engineering 50 Nanyang Avenue 639798 Singapore Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Sweden Digital Futures Stockholm10044 Sweden

This paper considers distributed online convex optimization with time-varying constraints. In this setting, a network of agents makes decisions at each round, and then only a portion of the loss function and a coordinate block of the constraint function are privately revealed to each agent. The loss and constraint functions are convex and can vary arbitrarily across rounds. The agents collaborate to minimize static network regret and network cumulative constraint violation. A novel distributed online algorithm with a vanishing stepsize is proposed and it achieves an O(Tmax {c,1-c}) static network regret bound and an O(T1-c/2) network cumulative constraint violation bound, where T is the number of rounds and c∈ (0,1) is a user-defined trade-off parameter. When Slater's condition holds (i.e, there is a point that strictly satisfies the inequality constraints), the network cumulative constraint violation bound is reduced to O(T1-c). Moreover, if the loss functions are strongly convex, then static network regret bound is reduced to O(log (T)), and the network cumulative constraint violation bound is reduced to O(√log (T)T) and O(log (T)) without and with Slater's condition, respectively. To the best of our knowledge, this paper is the first to achieve tighter (network) cumulative constraint violation bounds for (distributed) online convex optimization with time-varying constraints under Slater's condition. Finally, the theoretical results are verified through numerical simulations. © 1963-2012 IEEE.

关键词： Convex optimization

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Distributed Event-Triggered Bandit Convex Optimization With Time-Varying Constraints

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IEEE Transactions on Control of Network systems 2025年

作者： Zhang, Kunpeng Yi, Xinlei Wen, Guanghui Cao, Ming Johansson, Karl H. Chai, Tianyou Yang, Tao Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Ministry of Education Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai201210 China Southeast University School of Automation Nanjing210096 China University of Groningen Engineering and Technology Institute Groningen Faculty of Science and Engineering GroningenAG 9747 Netherlands KTH Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm10044 Sweden KTH Royal Institute of Technology Digital Futures Stockholm10044 Sweden

This paper considers the distributed bandit convex optimization problem with time-varying inequality constraints over a network of agents, where the goal is to minimize network regret and cumulative constraint violation. Existing distributed online algorithms solving this problem require that each agent broadcasts its decision to its neighbors at each iteration. However, communication resources are often limited. To better utilize communication resources, we propose a distributed event-triggered online primal-dual algorithm with two-point bandit feedback. Under several classes of appropriately chosen decreasing parameter sequences and non-increasing event-triggered threshold sequences, we establish dynamic network regret and network cumulative constraint violation bounds. These bounds are comparable to the results achieved by distributed event-triggered online algorithms with full-information feedback. Finally, a numerical example is provided to verify the theoretical results. © 2014 IEEE.

关键词： Optimization algorithms

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Distributed Constrained Online Nonconvex Optimization with Compressed Communication

arXiv

引用

arXiv 2025年

作者： Zhang, Kunpeng Xu, Lei Yi, Xinlei Cao, Ming Johansson, Karl H. Chai, Tianyou Yang, Tao State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Department of Mechanical Engineering University of Victoria VictoriaBCV8W 2Y2 Canada Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Shanghai201210 China Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen GroningenAG 9747 Netherlands Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures Stockholm10044 Sweden

This paper considers distributed online nonconvex optimization with time-varying inequality constraints over a network of agents. For a time-varying graph, we propose a distributed online primal–dual algorithm with compressed communication to efficiently utilize communication resources. We show that the proposed algorithm establishes an O(T max{1−θ1,θ1}) network regret bound and an O(T 1−θ1/2) network cumulative constraint violation bound, where T is the number of iterations and θ1 ∈ (0, 1) is a user-defined trade-off parameter. When Slater’s condition holds (i.e, there is a point that strictly satisfies the inequality constraints at all iterations), the network cumulative constraint violation bound is reduced to O(T 1−θ1). These bounds are comparable to the state-of-the-art results established by existing distributed online algorithms with perfect communication for distributed online convex optimization with (time-varying) inequality constraints. Finally, a simulation example is presented to validate the theoretical results. Copyright © 2025, The Authors. All rights reserved.

关键词： Convex optimization

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DYNAMICS AND CONTROL OF THE CZOCHRALSKI process .1. MODELING AND DYNAMIC CHARACTERIZATION

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JOURNAL OF CRYSTAL GROWTH 1987年第4期84卷 647-668页

作者： GEVELBER, MA STEPHANOPOULOS, G Department of Mechanical Engineering Laboratory for Intelligent Systems in Process Engineering Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Chemical Engineering Laboratory for Intelligent Systems in Process Engineering Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA

A nonlinear dynamic model of the Czochralski process, valid throughout the batch growth cycle is derived for use in designing an improved process controller. The model is a lumped element representation of the major system components, and simulates the dynamic system response to disturbances and system inputs. The linearized model is used to determine the system eigenstructure, revealing the system stability, transient response constants, and coupling. Significant results for control design include the identification of the basic time varying nature of the eigenstructure and the disturbances acting on the system, and identification of mechanisms that affect the transient system characteristics. In addition, the growth dynamic effects of liquid encapsulation, low thermal gradient schemes, and magnetic fields are discussed.

关键词： CRYSTALS

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Quantized Distributed Nonconvex Optimization Algorithms with Linear Convergence under the Polyak–Lojasiewicz Condition

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IEEE Transactions on Automatic Control 2025年

作者： Xu, Lei Yi, Xinlei Sun, Jiayue Shi, Yang Johansson, Karl H. Yang, Tao Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China University of Victoria Department of Mechanical Engineering VictoriaBCV8W 2Y2 Canada Tongji University Shanghai Institute of Intelligent Science and Technology Shanghai201804 China KTH Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm100 44 Sweden

This paper considers distributed optimization for minimizing the average of local nonconvex cost functions, by using local information exchange over undirected communication networks. To reduce the required communication capacity, we introduce an encoder–decoder scheme. By integrating it with distributed gradient tracking and proportional integral algorithms, respectively, we then propose two quantized distributed nonconvex optimization algorithms. Assuming the global cost function satisfies the Polyak–Lojasiewicz condition, which does not require the global cost function to be convex and the global minimizer is not necessarily unique, we show that our proposed algorithms linearly converge to a global optimal point. Moreover, we show that a low data rate is sufficient to guarantee linear convergence when the algorithm parameters are properly chosen. The theoretical results are illustrated by numerical examples. © 1963-2012 IEEE.

关键词： Cost functions

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A NEURAL NETWORK METHODOLOGY FOR process FAULT-DIAGNOSIS

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AICHE JOURNAL 1989年第12期35卷 1993-2002页

作者： VENKATASUBRAMANIAN, V CHAN, K Laboratory for Intelligent Process Systems School of Chemical Engineering Purdue University West Lafayette IN 47907

The ability of knowledge-based expert systems to facilitate the automation of difficult problems in process engineering that require symbolic reasoning and an efficient manipulation of diverse knowledge has generated considerable interest recently. Rapid deployment of these systems, however, has been difficult because of the tedious nature of knowledge acquisition, the inability of the system to learn or dynamically improve its performance, and the unpredictability of the system outside its domain of *** paper proposes a neural-network-based methodology for providing a potential solution to the preceding problems in the area of process fault diagnosis. The potential of this approach is demonstrated with the aid of an oil refinery case study of the fluidized catalytic cracking process. The neural-network-based system successfully diagnoses the faults it is trained upon. It is able to generalize its knowledge to successfully diagnose novel fault combinations it is not explicitly trained upon. Furthermore, the network can also handle incomplete and uncertain data. In addition, this approach is compared with the knowledge-based approach.

关键词： Chemical Operations

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ARTIFICIAL-INTELLIGENCE IN THE DEVELOPMENT AND DESIGN OF BIOCHEMICAL processES

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TRENDS IN BIOTECHNOLOGY 1986年第9期4卷 241-249页

作者： STEPHANOPOULOS, G STEPHANOPOULOS, G Laboratory for Intelligent Systems in Process Engineering Biotechnology Process Engineering Center Massachusetts Institute of Technology Cambridge MA 02139 USA

This article outlines the use of artificial intelligence in three areas of biotechnology: (a) exploration of new production routes for various bioproducts; (b) design of mammalian cell biofermentors; and (c) synthesis of downstream processing schemes for the separation and purification of proteins. Until recently, all of these areas have been ‘knowledge intensive’, driven by the incisive expertise of scientists and engineers, and quite resistant to analytic and rigorous mathematical formulations and solutions. Here we describe the ‘prototype intelligent system’ used in the above three areas, and attempt simple projections on the use of artificial intelligence in biotechnology.

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