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Parameter Estimation for batch Processes with Measurements of Large Sampling Intervals

IFAC-PapersOnLine 2015年第28期48卷 799-804页

作者： Zhao, Zhonggai Huang, Biao Liu, Fei Jiangnan University Wuxi China Department of Chemical and Materials Engineering University of Alberta Edmonton AB Canada

Parameter estimation of batch processes is investigated in this paper. Due to the lack of online sensors and short length of each batch, very few off-line laboratory analysis measurements are available with a large sampling interval within each batch. To capture the nonlinear and non-Gaussian features, dual particle filters are employed to perform the state estimation and the parameter estimation in parallel. Different from most of conventional methods for the parameter estimation which only employ the measurements along the time dimension (measurements of a single batch), the measurements along the batch dimension are also taken into account in this work. In the proposed method, to avoid the estimation degeneracy due to few measurements available along the time dimension, the parameter is treated as an invariant along the time dimension and its variability is introduced along the batch dimension denoted by a random walk model. Considering that different batches share the same raw material, states among different batches are related by slowly varying or constant initial states. The application in a beer fermentation process is used to illustrate the proposed approach. © 2015

关键词： Parameter estimation batch data processing Beer Brewing Monte Carlo methods State estimation batch process Conventional methods Laboratory analysis Nonlinear and non Gaussian Particle filter Random walk modeling Recursive state estimation Sampling interval

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Hierarchical Optimal Operation of Continuous-batch Processes

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IFAC-PapersOnLine 2015年第23期48卷 294-301页

作者： De Prada, C. Cristea, S.P. Mazaeda, R. Dpt. of Systems Engineering and Automatic Control University of Valladolid Valladolid47011 Spain

This paper presents a methodology for the optimal operation of mixed continuous-batch processes based on a hierarchical decomposition of the problem: local dynamic optimizers perform the economic optimization of the batch units or continuous plants, while the upper layer deals with the scheduling of the batch units and the coordination of the flows and properties of sub-processes using a novel simplified model built with predictions from the lower level. The approach is illustrated with a case study from the sugar industry corresponding to the joint operation of the evaporation and crystallization sections operated to maximize production with minimum energy consumption. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Sugar industry batch data processing Energy utilization Hierarchical systems Optimization Scheduling algorithms Continuous batch Continuous plant Economic optimization Hierarchical control Hierarchical decompositions Minimum energy consumption NMPC Optimal operation

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Quality Analysis and Prediction for Start-up Process of Injection Molding Processes

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IFAC-PapersOnLine 2018年第18期51卷 233-238页

作者： Zou, Mingjun Zhao, Luping Wang, Shu Chang, Yuqing Wang, Fuli College of Information Science and Engineering Northeastern University Shenyang China

As a typical batch process, injection molding process plays an important role in industry. This work focuses on the start-up process of injection molding process. Based on a deep study of the start-up process of injection molding process, a phase-shift sliding window modelling scheme is proposed in this paper for quality prediction. Firstly, to deal with the time-varying problem during the start-up process, sliding windows are built in the batch direction, and multiple continuous models are established to capture the relationship between the process variables and the quality respectively. Secondly, according to the operational characteristics of the plastication phase of injection molding process, a hypothesis that the plastication phase of the current batch has a greater impact on the quality of the next batch than the quality of the current batch is proposed, and the existence of this assumption is verified by the simulation with experimental data. Under the premise of the above assumptions, a new quality prediction scheme is proposed and is verified to be more accurate than the traditional methods. © 2018

关键词： Injection molding batch data processing Communication channels (information theory) Decoding Forecasting Continuous models Injection molding process Multiphase batch process Operational characteristics Process Variables Quality prediction Sliding Window Start up process

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Strategies for Big data Analytics through Lambda Architectures in Volatile Environments

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IFAC-PapersOnLine 2016年第30期49卷 114-119页

作者： Alexandre da Silva, Veith Julio C.S. dos, Anjos Edison Pignaton, de Freitas Thomas J., Lampoltshammer Claudio F., Geyer Porto AlegreRS15064 Brazil Danube University Krems Department for E-Governance and Administration Dr.-Karl-Dorrek-Str. 30 Krems3500 Austria

Expectations regarding the future growth of Internet of Things (IoT)-related technologies are high. These expectations require the realization of a sustainable general purpose application framework that is capable to handle these kind of environments with their complexity in terms of heterogeneity and volatility. The paradigm of the Lambda architecture features key characteristics (such as, robustness, fault tolerance, scalability, generalization, extensibility, ad-hoc queries, minimal maintenance, and low-latency reads and updates) to cope with this complexity. The paper at hand suggest a basic set of strategies to handle the arising challenges regarding the volatility, heterogeneity, and desired low latency execution by reducing the overall system timing (scheduling, execution, monitoring, and faults recovery) as well as possible faults (churn, no answers to executions). The proposed strategies make use of services such as migration, replication, MapReduce simulation, and combined processing methods (batch- and streaming-based). Via these services, a distribution of tasks for the best balance of computational resources is achieved, while monitoring and management can be performed asynchronously in the background. © 2016

关键词： Big data batch data processing Cloud computing Fault tolerance Internet of things processing Scheduling Application frameworks Computational resources Grid computin Internet of Things (IOT) Key characteristics Monitoring and management Stream processing Volatile environments

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PI-Based Set-Point Learning Control for batch Processes with Unknown Dynamics and Nonrepetitive Uncertainties

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IEEE Transactions on Automatic Control 2024年第6期70卷 3649-3664页

作者： Hao, Shoulin Liu, Tao Rogers, Eric Wang, Youqing Paszke, Wojciech Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Eduction The Institute of Advanced Measurement and Control Technology Dalian116024 China Beijing University of Chemical Technology College of Information Science and Technology Beijing100029 China University of Zielona Góra Institute of Automation Electronic and Electrical Engineering Zielona Góra65-246 Poland

For industrial batch processes with unknown dynamics subject to nonrepetitive initial conditions and disturbances, this paper develops a novel adaptive data-driven set-point learning control (ADDSPLC) scheme based on only the measured process input and output data, which has two loops, one for the dynamics within a batch and the other for the batch-to-batch dynamics. In the former case, a model-free tuning strategy is firstly presented for determining the closed-loop PI controller parameters. For the latter case, a set-point learning control law with adaptive set-point learning gain and gradient estimation is developed for batch run optimization. Robust convergence of the output tracking error is rigorously analyzed together with the boundedness of adaptive learning gain and real-time updated set-point command. Moreover, another iterative extended state observer based (ADDSPLC) scheme is developed with rigorous convergence and boundedness analysis, to enhance the robust tracking performance against nonrepetitive uncertainties. Finally, two illustrative examples from the literature are used to demonstrate the effectiveness and superiority of the new schemes over the recently developed data-driven learning control designs. © 1963-2012 IEEE.

关键词： batch data processing

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Summingbird: A Framework for Integrating batch and Online MapReduce Computations

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PROCEEDINGS OF THE VLDB ENDOWMENT 2014年第13期7卷 1441-1451页

作者： Boykin, Oscar Ritchie, Sam O'Connell, Ian Lin, Jimmy Twitter Inc San Francisco CA 94103 USA

Summingbird is an open-source domain-specific language implemented in Scala and designed to integrate online and batch MapReduce computations in a single framework. Summingbird programs are written using dataflow abstractions such as sources, sinks, and stores, and can run on different execution platforms: Hadoop for batch processing (via Scalding/Cascading) and Storm for online processing. Different execution modes require different bindings for the dataflow abstractions (e.g., HDFS files or message queues for the source) but do not require any changes to the program logic. Furthermore, Summingbird can operate in a hybrid processing mode that transparently integrates batch and online results to efficiently generate up-to-date aggregations over long time spans. The language was designed to improve developer productivity and address pain points in building analytics solutions at Twitter where often, the same code needs to be written twice (once for batch processing and again for online processing) and indefinitely maintained in parallel. Our key insight is that certain algebraic structures provide the theoretical foundation for integrating batch and online processing in a seamless fashion. This means that Summingbird imposes constraints on the types of aggregations that can be performed, although in practice we have not found these constraints to be overly restrictive for a broad range of analytics tasks at Twitter.

关键词： batch data processing Abstracting Problem oriented languages Social networking (online) Algebraic structures Domain specific languages Execution platforms Hybrid processing Message queues Online processing Program logic Theoretical foundations

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A Pattern-based Method for Scheduling of Energy-integrated batch Process Networks

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IFAC-PapersOnLine 2016年第7期49卷 669-674页

作者： Mete, Shrikant Jogwar, Sujit S. Department of Chemical Engineering Institute of Chemical Technology Mumbai400019 India

This paper describes a pattern-based method for the generation of flexible-horizon production schedules for energy-integrated batch systems using a benchmark reactor-distillation system. The proposed methodology consists of structural decomposition of an already existing schedule for a fixed time horizon to identify repetitive patterns. It is found that such a schedule can be divided into three sections - initial, final and an intermediate section - of manipulable sizes. Based on such a decomposition, it is possible to generate an extended (or shorter) horizon schedule by increasing (or decreasing) the sizes of these sections. The proposed method allows for the generation of new schedules without solving mathematically rigorous mixed integer programming problems. The key advantage of the proposed method is the significant reduction in the time required for solving large scheduling problems, especially for online rescheduling applications. The effectiveness of the proposed methodology is illustrated through the same benchmark scheduling problem. © 2016

关键词： Scheduling algorithms batch data processing Distillation Distillation equipment Integer programming Problem solving Production control Scheduling batch rescheduling batch scheduling Distillation systems Heat integration Mixed integer programming Pattern based method Pattern identification Structural decomposition

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Phase partition for nonlinear batch process monitoring

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IFAC-PapersOnLine 2016年第7期49卷 1181-1186页

作者： Liu, Jingxiang Liu, Tao Zhang, Jie Institute of Advanced Control Technology Dalian University of Technology Dalian116024 China School of Chemical Engineering and Advanced Materials Newcastle University Newcastle upon Tyne NE1 7RU United Kingdom

A window-based step-wise sequential phase partition method is proposed for monitoring nonlinear batch processes with multiphase operations. The three-dimensional batch data are unfolded in the batch-wise direction to facilitate establishing the kernel principle component analysis (KPCA) models. A moving window is introduced to maintain the kernel parameters unchanged for computation while preserving the time sequence of different phases, thus capable of significantly reducing the storage space of kernel matrices and the computation complexity. A numerical case and a penicillin fermentation process are used to demonstrate the effectiveness and merit of the proposed method. © 2016

关键词： batch data processing Digital storage Drug products Numerical methods Principal component analysis Process control Process monitoring batch process batch process monitoring Computation complexity Kernel principle component analysis Moving window Penicillin fermentation process Phase partition Phase partition methods

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Dynamic optimization of fixed bed chemical-looping combustion systems integrated in thermal power plants

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IFAC-PapersOnLine 2016年第7期49卷 115-120页

作者： Han, Lu Bollas, George M. Department of Chemical & Biomolecular Engineering University of Connecticut Storrs 191 Auditorium Road Unit 3222 CT06269-3222 United States

Optimization methods can be used to improve the design and operation of batch processes. In this work, we illustrate the application of dynamic simulation and optimization for power generation in chemical-looping combustion systems, carried out in batch reactors. We present a framework for the optimization strategy, in which the objective is to maximize a measure of the energy efficiency, bounded by inequality constraints reflecting the industry standards. Several case studies are used to illustrate the applicability of the present problem formulation to enhance the process performance. © 2016

关键词： Combustion batch data processing batch reactors Constraint theory Dynamic models Energy efficiency Fuel systems Power generation Thermoelectric power plants Chemical looping combustion Design and operations Dynamic optimization Inequality constraint Optimization problems Optimization strategy Simulation and optimization Thermal power plants

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Improving the software engineering of brew house plants by modularizing the control software

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IFAC-PapersOnLine 2018年第10期51卷 241-248页

作者： Brunner, Benjamin Aicher, Thomas Gemein, Fabian Vogel-Heuser, Birgit Institute of Automation and Information Systems Technische Universität München Germany

The development of control software for automation plants is a time-consuming task. With a modular structure of the control software, which is derived from a functional decomposition of plant and process, this paper presents an alternative approach to the widely used procedure “Copy, paste, modify” of the control software of a similar plant. The approach is applied on batch process plants, in particular 2-vessel brew house plants. Their plant topologies are modularized with regard to hardware, process and control. Based on the established modular structure of the plant control, software modules are developed. Generating the control software of new plants from an assembly of these software modules will reduce implementation effort and improve software reusability. As a proof of concept, a prototypical test plant is constructed and run with control software, which was automatically generated from a prototypical tool implementation. © 2016

关键词： Computer control Automatic test pattern generation batch data processing Computer software reusability Process control Programmable logic controllers Reusability Software testing Structure (composition) Automatically generated Control software Functional decomposition Model driven Engineering Modular structures Proof of concept Software modules Time consuming tasks

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