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13th IFAC symposium on dynamics and control of process systems, including Biosystems (DYCOPS)

作者： Kim, Boeun Heo, Seongmin Ryu, Kyung Hwan Princeton Univ Andlinger Ctr Energy & Environm Princeton NJ 08544 USA Dankook Univ Dept Chem Engn Yongin 16890 South Korea Sunchon Natl Univ Dept Chem Engn Sunchon 57922 South Korea

Conceptual process design is a key step to the commercialization of novel technologies, where a network of reaction-separation-recycle processes is synthesized to produce target products from given raw materials. Such design is often realized using commercial process simulators (e.g., Aspen Plus, gPROMS) to calculate mass and energy balances, which can then be used as a basis for the evaluation through, for example, techno-economic analysis and life cycle assessment. In such evaluation, the reference design is very important as it forms the foundation for further analysis such as sensitivity analysis. However, it is typically determined heuristically, leading to a design with suboptimal (or even poor) evaluation metrics. To address this problem, in this work, model-free design of experiment methods are implemented to optimize process designs developed using Aspen Plus. Performances of different methods are compared using representative chemical process examples. Copyright (C) 2022 The Authors.

关键词： process design framework Model-free optimization Response surface method Central composite design Techno-economic analysis

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Probabilistic dynamic-controlled latent variable model for pattern-space modelling and pattern-based stochastic model predictive control 13th

Probabilistic dynamic-controlled latent variable model for p...

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13th IFAC symposium on dynamics and control of process systems, including Biosystems (DYCOPS)

作者： Zheng, Niannian Shardt, Yuri A. W. Luan, Xiaoli Liu, Fei Tech Univ Ilmenau Dept Automat Engn D-98693 Ilmenau Germany Jiangnan Univ Dept Automat Wuxi 214122 Jiangsu Peoples R China

Industrial processes are measured and controlled using high-dimensional process variables, but its overall operation is usually characterised by low-dimensional patterns. The changes in the pattern are dominated by three features: free motion, controlled motion, and uncertainty. In this paper, all three features are taken into consideration to propose a new probabilistic dynamic-controlled latent variable (PDCLV) model structure using a dynamic Bayesian network for process modelling in the pattern space. To this end, the linear dynamic system characterised by control inputs is introduced, and the expectation maximisation algorithm is specially designed for learning the PDCLV model. Benefitting from the dynamic causality between control inputs and the explicit modelling of the pattern, a method for pattern-based stochastic model predictive control (SMPC) is implemented successfully to realise process optimisation. A case study on an industrial boiler combustion process demonstrates the benefits of the proposed PDCLV structure for pattern-space modelling and pattern-based SMPC. Copyright (C) 2022 The Authors.

关键词： Dynamic controlled latent variable pattern space modelling dynamic Bayesian network linear dynamic system with control inputs expectation maximisation stochastic model predictive control

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Feedback control strategy for optimizing biohydrogen production from organic solid waste in a discontinuous process

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2021年第72期46卷 35831-35839页

作者： Jimenez-Ocampo, Ulises E. Santiago, Sonia G. Vargas, Alejandro Moreno-Andrade, Ivan Univ Nacl Autonoma Mexico Unidad Acad Juriquilla Inst Ingn Lab Res Adv Proc Water Treatment Blvd Juriquilla 3001 Queretaro 76230 Mexico

The use of control strategies for the biological process of hydrogen (H-2) production can increase productivity, stability, and robustness, increasing the viability of the implementation of these systems at the industrial level. A feedback control strategy for optimizing biohydrogen production from organic solid waste in a discontinuous process was proposed and pragmatically tested. The control strategy, which is based on recursively fitting a modified Gompertz model to gathered online data of the cumulative H-2 volume, could eventually stabilize the system at an optimal reaction time. The control strategy was evaluated during 140 degradation cycles maintaining a stable H-2 production with a reproducible form in the cumulative H-2 production curve. Independent of the changes in the microbial population dynamics in the reactor, the control strategy maintained a stable H-2 production avoiding the presence of methanogenic archaea. With the proposed controller, a compromise between the needed HRT and the H-2 production was obtained. (C) 2021 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Biohydrogen Feedback control strategy Gompertz curve Organic solid waste process control SBR

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Open-Loop Analysis for the Stability of Reactive Distillation Columns with Intermediate Condensers for the Production of Silane 13th

Open-Loop Analysis for the Stability of Reactive Distillatio...

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13th IFAC symposium on dynamics and control of process systems, including Biosystems (DYCOPS)

作者： Alcantara-Avila, J. Rafael Cabrera-Ruiz, Julian Lee, Hao-Yeh Kyoto Univ Dept Chem Engn Kyoto Japan Univ Guanajuato Dept Ingn Quim Guanajuato Mexico Natl Taiwan Univ Sci & Technol Dept Chem Engn Taipei Taiwan

The production of cost-effective Silane is paramount for reducing the cost in the solar photovoltaic industry to make it a more competitive renewable energy source. Thus, the production of Silane through reactive distillation (RD) is an alternative that reduces the equipment size, cost, and operation in comparison with a typical reaction-separation process mainly because the chemical reactions are limited by equilibrium. This work presents an open-loop analysis for RD columns with two intermediate condensers (RD-2IC) and different heat distribution in the inter condensers (IC). The open-loop stability of the RD columns analysis is done to understand the relationship between heat distribution at IC and their theoretical control properties to determine which heat distribution conditions result in stable operation. The results showed that RD-2IC columns attained economic savings compared to the RD column. Also, the optimal RD-2IC has better control properties than the RD column in an open-loop analysis. Copyright (C) 2022 The Authors.

关键词： Reactive Distillation control properties SVD process control process simulation

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Model Predictive controller Design For Bioprocesses Based On Machine Learning Algorithms 13th

Model Predictive Controller Design For Bioprocesses Based On...

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13th IFAC symposium on dynamics and control of process systems, including Biosystems (DYCOPS)

作者： Rashedi, Mohammad Khodabandehlou, Hamid Demers, Matthew Wang, Tony Garvin, Christopher Amgen Inc Digital Integrat & Predict Technol Thousand Oaks CA 91320 USA Amgen Inc Digital Integrat & Predict Technol 40 Technol Way West Greenwich RI 02817 USA

The optimization of critical quality attributes in biopharmaceutical processes demands the development of a scalable and optimal control scheme to meet the process constraints and objectives. In this paper, we designed a model predictive controller (MPC) to find the optimal feeding strategy to maximize cell growth and metabolite production in fed-batch bioprocesses. Due to high complexity of bioprocesses and lack of high-fidelity first principle models, we evaluated the use of machine learning algorithms in the forecast model to aid in our development. By taking advantage of the bioprocess model, this controller aims to maximize the protein production daily for each batch. The control scheme of the bioprocess is defined as an optimization problem to be solved while all metabolites and cell culture process variables are maintained within the specification. To evaluate the performance of the controller, we designed and implemented MPC with the best model to a bioreactor in a real experiment. The experimental validation confirms more than 2% improvement in final protein production compared to average historical experiments. Copyright (C) 2022 The Authors.

关键词： Machine learning Model predictive control Bio-pharmaceutical process Optimal control Linear MPC

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Centralized optimization of resource routing in interconnected food production units with harvesting events 13th

Centralized optimization of resource routing in interconnect...

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13th IFAC symposium on dynamics and control of process systems, including Biosystems (DYCOPS)

作者： Al Khatib, Mohammad Hempel, Arne-Jens Padmanabha, Murali Streif, Stefan Tech Univ Chemnitz Automat Control & Syst Dynam Lab Chemnitz Germany

In this work we synthesize constrained control laws monolithically to maximize production for an interconnected food production control system. The system we consider consists of a set of several plant and larvae units interconnected through valves in a circular topology. We synthesize then a centralized MPC controller after formulating the problem in the form of a nonlinear mixed integer optimization program. Then we show that the overall production increases and the energy costs decrease when the production units are connected together compared to the case when they are decoupled. These results are shown in an illustrative example on an interconnection of two production units: one containing plants (lettuce) and another containing larvae insects (Hermetia illucens).

关键词： model-predictive control interconnected system food production units impulsive systems model-based control

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Development of Wiener-Hammerstein Models Parameterized using Orthonormal Basis Filters and Deep Neural Network 13th

Development of Wiener-Hammerstein Models Parameterized using...

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13th IFAC symposium on dynamics and control of process systems, including Biosystems (DYCOPS)

作者： Patel, Janak M. Kumar, Kunal Patwardhan, Sachin C. Indian Inst Technol Dept Chem Engn Mumbai Maharashtra India Indian Inst Technol Dept Syst & Control Engn Mumbai Maharashtra India

Many chemical or biochemical processes exhibit strongly nonlinear dynamic behavior in the desired region of operations. To develop effective monitoring and control schemes for such systems, it is necessary to develop a reliable model that captures dynamics as well as the steady-state behavior over a wide range of operations. In this work, it is proposed to develop a block-oriented Wiener-Hammerstein model parameterized using generalized orthonormal basis filters and deep neural network (GOBF DNN). A two-step procedure is developed to select the generalized orthonormal basis filters (GOBF) pole locations and estimate the deep neural network (DNN) parameters. The efficacy of the proposed modeling strategy is demonstrated using the simulation study on a benchmark continuously operated fermenter system. The proposed GOBF DNN model is able to capture the dynamic and steady-state behavior of the plant over a wide range of operations. Comparison of performances based on the dynamic as well as the steady-state indices clearly underscores the advantages of using a DNN over a shallow neural net and a NARX model developed using DNN. Copyright (C) 2022 The Authors.

关键词： Block Oriented Model Weiner-Hammerstein Model Deep Neural Network Generalized Orthonormal Basis Filters

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Deep Reinforcement Learning Based Automatic control in Semi-Closed Greenhouse systems 13th

Deep Reinforcement Learning Based Automatic Control in Semi-...

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13th IFAC symposium on dynamics and control of process systems, including Biosystems (DYCOPS)

作者： Ajagekar, Akshay You, Fengqi Cornell Univ Ithaca NY 14853 USA

This work proposes a novel deep reinforcement learning (DRL) based control framework for greenhouse climate control. This framework utilizes a neural network to approximate state-action value estimation. The neural network is trained by adopting a Q-learning based approach for experience collection and parameter updates. Continuous action spaces are effectively handled by the proposed approach by extracting optimal actions for a given greenhouse state from the neural network approximator through stochastic gradient ascent. Analytical gradients of the state-action value estimate are not required but can be computed effectively through backpropagation. We evaluate the performance of our DRL algorithm on a semi-closed greenhouse simulation located in New York City. The obtained computational results indicate that the proposed Q-learning based DRL framework yields higher cumulative returns. They also demonstrate that the proposed control technique consumes 61% lesser energy than deep deterministic policy gradient (DDPG) method.

关键词： Deep reinforcement learning Greenhouse Climate control

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A Stochastic Optimization based on Sample Average Approximation for a Boiler process 13th

A Stochastic Optimization based on Sample Average Approximat...

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13th IFAC symposium on dynamics and control of process systems, including Biosystems (DYCOPS)

作者： de Almeida, Gustavo Matheus Park, Song Won Lee, Chang Jun Univ Fed Minas Gerais Sch Engn Dept Chem Engn Belo Horizonte MG Brazil Univ Sao Paulo Polytech Sch Dept Chem Engn Sao Paulo Brazil Pukyong Natl Univ Dept Safety Engn Busan South Korea

The goal of this study is to investigate stochastic optimal solutions for a boiler process in a pulp mill. The objective function is a steam generation while two pollutant emissions should be complied with their regulations. Support Vector Regression (SVR) is employed to build empirical models for representing a boiler process and air temperatures are considered as uncertainties To make stochastic problems, Sample Average Approximation (SAA) based on Monte-Carlo sampling is introduced and Particle Swarm Optimization (PSO) technique is applied to investigate stochastic solutions. The results show that the stochastic optimal solutions can provide improved performances compared to the deterministic approach. Copyright (C) 2022 The Authors.

关键词： Stochastic Optimization SAA PSO SVM Chemical recovery boiler Pulp mill

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Simulation of a System for Reducing Gases Emitted in a Steel Casting process by Two-Stage Centrifugal Separation 7th

Simulation of a System for Reducing Gases Emitted in a Steel...

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7th Brazilian Technology symposium (BTSym) - Emerging Trends and Challenges in Technology

作者： Mulatillo, Alberto Sernaque, Carla Ronceros, Julio Vinces, Leonardo Univ Peruana Ciencias Aplicadas Sch Mechatron Engn Lima Peru

ISBN: (纸本)9783031085451;9783031085444

This study proposes the simulation of a system for reducing polluting gases produced by a steel foundry by two-stage centrifugation. This simulation seeks to verify the physical principle of centrifugation in two cyclones (Stairmand and Swift types) in serial connection: one with its inlet at the outlet of the other, based on initial gas inlet conditions, as well as the design equations of this equipment. Likewise, it's intended to demonstrate the efficiency of this system by entering the mass composition of the gas, in particulate matter (PM) terms, measuring its new composition at the output, after simulating the gas and particle dynamics. For this, Ansys CFD and Ansys Fluent software are used (with previous data analysis and processing), and Python programming techniques to automate calculations and obtain the optimal design values for the cyclones. This system seeks to be an alternative to a universe of expensive and enormous equipment related to environmental pollution control.

关键词： Dual cyclone Pollution Contamination CFD DPM Rosim-Rammler

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