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Improved batch Process Monitoring and Diagnosis Based on Multiphase KECA

IFAC-PapersOnLine 2018年第18期51卷 827-832页

作者： Qi, Yongsheng Wang, Yuan Lu, Chenxi Wang, Lin Inner Mongolia University of Technology HuhhotInner Mongolia010051 China

Multiple phases with transitions from phase to phase are important characteristics of many batch processes. The linear characteristics of batch processes are usually taken into consideration in the traditional algorithms while the nonlinearity is neglected. However, to monitor batch processes more accurately and efficiently, such process features are needed to be considered carefully. In this paper, a new similarity index based on KECA (kernel entropy component analysis) is defined for batch processes with nonlinear characteristics. A new phase division and monitoring method based on the proposed similarity index is brought forward simultaneously. First, nonlinear characteristics can be extracted in feature space via performing KECA on each preprocessed time-slice data matrix. Then phase division is achieved with the similarity change of the extracted feature information. By establishing a series of KECA models for transitions and steady phases, it reflects the diversity of transitional characteristics objectively and can preferably solve the stage-transition monitoring problem in multistage batch processes. Finally, in order to overcome the problem that the traditional contribution plot cannot be applied to the kernel mapping space, a nonlinear contribution plot diagnosis algorithm is proposed. Both results of simulation study and industrial application clearly demonstrate the effectiveness and feasibility of the proposed method. © 2018

关键词： batch data processing Failure analysis Process monitoring batch process batch process monitoring Fault monitoring KECA Linear characteristics Multi stage batch process Nonlinear characteristics Nonlinear contributions

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batch-to-batch Optimization of Chemical Processes in the Presence of Model-Plant Mismatch Using a Variable Subset of Model Parameters

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IFAC-PapersOnLine 2016年第7期49卷 61-66页

作者： Hille, Rubin Budman, Hector M. Department of Chemical Engineering University of Waterloo Waterloo ON N2L 3G1 Canada

In the presence of model-plant mismatch, a standard “two-step” approach, involving repeated identification and optimization steps, cannot guarantee convergence to the process optimum. Model parameter adaptation can be used for handling model error by correcting for mismatch between predicted and measured cost and constraint gradients while simultaneously satisfying both identification and optimization objectives. However, updating all model parameters at once is often impractical due to estimability and increased sensitivity to noise. This work presents a procedure for selecting, after each run, a particular subset of parameters based on parametric sensitivity of the model output and of cost and constraint gradients. The resulting improvements with respect to previous run-to-run studies are illustrated using a simulated case study of a penicillin fed-batch process. © 2016

关键词： Sensitivity analysis batch data processing batch optimization Constraint gradients Fed batch process Model based optimization Model corrections Parameter adaptation Parameter selection Parametric sensitivity

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Multiresolution Analytics for Large Scale Industrial Processes

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IFAC-PapersOnLine 2018年第18期51卷 464-469页

作者： Reis, Marco S. Rato, Tiago J. CIEPQPF Department of Chemical Engineering University of Coimbra Pólo II Rua Sílvio Lima Coimbra3030-790 Portugal

data collected from Industry 4.0 scenarios present a variety of data structures, reflecting the evolution of industrial processes, measurement systems and IT infrastructures (“variety” is actually one of the 4 V's of Big data, meaning that its existence is widely recognized). data analytics platforms must adapt to this context and keep the pace of its evolution, in order to continue providing effective solutions to practitioners for dealing with the large data resources now available. In this context, one prevalent feature of industrial data has been largely overlooked: their multiresolution nature. The multiresolution nature of data is directly connected to their granularity in the time domain, an aspect that induces inner dependencies that current frameworks cannot address in a consistent and rigorous way. Furthermore, multiresolution has been often mistaken as a simple multirate scenario, where in fact the meaning of the observations is completely different. In this paper, we highlight such differences and discuss current multiresolution frameworks for effectively handling industrial data sets. © 2018

关键词： Big data batch data processing data handling Kalman filters Process monitoring batch process Continuous process Industrial processs IT infrastructures Large scale industrial process Measurement system Multiresolution data Soft sensors

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A robust NMPC scheme for semi-batch polymerization reactors

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IFAC-PapersOnLine 2016年第7期49卷 37-42页

作者： Jang, Hong Lee, Jay H. Biegler, Lorenz T. Department of Biomolecular and Chemical Engineering Korea Advanced Institute of Science and Technology 291 Daehak-ro Yuseong-gu Daejeon34141 Korea Republic of Chemical Engineering Department Carnegie Mellon University PittsburghPA 15213 United States

A robust nonlinear model predictive control (NMPC) scheme is proposed for batch processes with multiple types of uncertainties. Recently, economic MPC (eMPC) has attracted significant attention, particularly for batch process control given its flexibility in the cost function while addressing the nonlinear constrained multivariable dynamics seen in most batch processes. However, in the presence of various uncertainties such as parameter errors, external disturbances, and noise, performance of eMPC can deteriorate significantly as it tends to drive the system to limits of constraints. To achieve constraint satisfaction in the presence of common uncertainties, we propose a robust NMPC method based on multistage scenarios, state estimation, and back-off constraints. Performance of the proposed robust NMPC scheme is evaluated through an example of anionic propylene oxide polymerization reactor. © 2016

关键词： batch reactors Anionic polymerization batch data processing Cost functions Model predictive control Nonlinear systems Predictive control systems State estimation Stochastic models Stochastic programming Stochastic systems Backoff batch process batch process control Constraint Satisfaction External disturbances Nonlinear model predictive control Polymerization reactors Semi batch polymerization

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Dual robust control of batch processes based on optimality-conditions parameterization

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IFAC-PapersOnLine 2018年第18期51卷 774-779页

作者： Paulen, Radoslav Fikar, Miroslav Faculty of Chemical and Food Technology Slovak University of Technology in Bratislava Slovakia

This paper presents a scheme for dual robust control of batch processes under parametric uncertainty. Some recently proposed approaches can be used to tackle this problem, however, this will be done at the price of conservativeness or significant computational burden. In order to increase computational efficiency, we propose a scheme that uses parametrized conditions of optimality in the adaptive predictive-control fashion. The dual features of the controller, i.e., balance between the control moves that excite the system to improve accuracy of the parameter estimation and between the moves that optimize process performance, is realized through scenario-based (multi-stage) approach, which allows for modeling of the adaptive robust decision problem and for projecting this decision into predictions of the controller. The proposed approach is illustrated on a case study from batch membrane filtration. © 2018

关键词： Process control Adaptive control systems batch data processing Computational efficiency Controllers Decision theory Microfiltration Parameter estimation Robust control Adaptive Control batch control Membrane separation Pontryagin's minimum principles Predictive control

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Experimental Design in Simultaneous Identification and Optimization of batch Processes under Model-Plant Mismatch

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IFAC-PapersOnLine 2018年第18期51卷 797-802页

作者： Hille, Rubin Budman, Hector M. Department of Chemical Engineering University of Waterloo WaterlooON N2L 3G1 Canada

Model-plant mismatch commonly arises from simplifications and assumptions during the development of first-principles models. Hence, when employing such models in iterative optimization schemes, structural mismatch may lead to inaccurate prediction of the necessary conditions of optimality. This results in convergence to a predicted optimum which does not coincide with the actual process optimum. The method of simultaneous identification and optimization aims to correct for errors in the predicted gradients of the cost and constraints by adapting the model parameters. In a former implementation of this approach, the gradients have been corrected only locally at the current operating point. To achieve a better prediction of the cost function over a wider range of input conditions, we propose to consider cost measurements from previous batch experiments combined with an optimal experimental design of future experiments. Using this approach, it is possible to achieve a better prediction, especially around the optimum, and to make the gradient correction step less susceptible to uncertainty in local gradient measurements. The improvements are illustrated using a simulated run-to-run optimization study of a cell-culture process. © 2018

关键词： Design of experiments batch data processing Cell culture Cost functions Forecasting Iterative methods Statistics Structural optimization Uncertainty analysis batch process First principles models Iterative Optimization Model corrections Necessary conditions of optimality Optimal experimental designs Run to run optimization Simultaneous identification

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Performance Challenges and Solutions in Big data Platform Hadoop

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Recent Advances in Computer Science and Communications 2023年第9期16卷 27-41页

作者： Singh, Balraj Verma, Harsh K. Madaan, Vishu Department of Computer Science and Engineering Dr. B.R. Ambedkar NIT Jalandhar India School of Computer Science and Engineering Lovely Professional University Phagwara India

Background: The present era demands continuous support to bring improvements in executing complex analytics on large-scale data and to work beyond traditional systems. Objective: The need for processing diverse data types and solutions for different domains of the industry is rising. Such needs increase the requirement for sophisticated techniques and methods to enhance the existing platforms and mechanisms further. It provides an opportunity for the research community to investigate further into the existing systems, find potential issues, and propose new ways to improve the current systems. Hadoop is a popular choice to manage and process Big data. It is an open-source platform and a front-runner in the batch processing of large-scale jobs. The economy associated with the cluster in scaling is low as compared to other platforms. However, this popularity by no means guarantees high performance in all scenarios. With the continuous evolution in data development and industrial requirements, it is imperative to investigate and look into new methods and techniques to bring advancements to the existing system. Method: A systematic review is represented in this paper to have an insight into the current progress in this field. Research publications from various sources are taken and analyzed. The performance of a cluster largely depends upon the different job processing mechanisms and policies associated with it. Conclusion: While extensive studies and solutions are proposed, the performance bottlenecks in terms of load balancing, resource utilization, content management, and efficient processing prevail. Not many of the solutions are there on scheduling about the trade-off between different parameters, the process of content splitting and merging is not explored to a large extent and the skew mitigation solutions are more focused on Reduce side of the MapReduce while the Map side is not utilized much for load balancing. © 2023 Bentham Science Publishers.

关键词： batch data processing

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Protecting-group-free synthesis of hydroxyesters from amino alcohols

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CHEMICAL COMMUNICATIONS 2020年第74期56卷 10938-10941页

作者： Reynard, Guillaume Joseph-Valcin, Eve-Marline Lebel, Helene Univ Montreal Dept Chem Montreal PQ H3C 3J7 Canada Univ Montreal Ctr Green Chem & Catalysis CGCC Montreal PQ H3C 3J7 Canada

The synthesis of hydroxyesters from carboxylic acids and unprotected amino alcohols in both continuous flow and batch processes is reported. The formation of a transient diazonium species with a dinitrite reagent is k... 详细信息

关键词： batch data processing

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Matrix bloom filter: An efficient probabilistic data structure for 2-tuple batch lookup

arXiv

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arXiv 2019年

作者： Fu, Yue Du, Rong Hu, Haibo Au, Man Ho Li, Dagang School of ECE Shenzhen Graduate School Peking University China Department of Computing Hong Kong Polytechnic University Hong Kong China Department of Electronic and Information Engineering Hong Kong Polytechnic University Hong Kong China

—With the growing scale of big data, probabilistic structures receive increasing popularity for efficient approximate storage and query processing. For example, Bloom filters (BF) can achieve satisfactory performance for approximate membership existence query at the expense of false positives. However, a standard Bloom filter can only handle univariate data and single membership existence query, which is insufficient for OLAP and machine learning applications. In this paper, we focus on a common multivariate data type, namely, 2-tuples, or equivalently, key-value pairs. We design the matrix Bloom filter as a high-dimensional extension of the standard Bloom filter. This new probabilistic data structure can not only insert and lookup a single 2-tuple efficiently, but also support these operations efficiently in batches — a key requirement for OLAP and machine learning tasks. To further balance the insertion and query efficiency for different workload patterns, we propose two variants, namely, the maximum adaptive matrix BF and minimum storage matrix BF. Through both theoretical and empirical studies, we show the performance of matrix Bloom filter is superior on datasets with common statistical distributions;and even without them, it just degrades to a standard Bloom filter. Copyright © 2019, The Authors. All rights reserved.

关键词： batch data processing

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Faster BERT with Double Loss Function Based on batch data Preprocessing

Faster BERT with Double Loss Function Based on Batch Data Pr...

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作者： Pengqi Duan Yudong Zheng Jun Lu College of Computer Science and Technology Heilongiiang University Heilongjiang UniversityHLJU College of Computer Science and Technology Heilongjiang University Heilongjiang UniversityHLJU College of Computer Science and Technology Heilongiiang University Key Laboratory of Database and Parallel Computing of Heilongjiang Province Heilongjiang University Heilongjiang UniversityHLJU

Even though pre-trained language models like BERT and XLNet have produced significant consequences on a variety of tasks of natural language processing,they are difficult to deploy in practical applications due to their large number of parameters,and huge calculation and memory *** order to improve the training speed and inference speed of the pretrained model,the FasterBERT model is proposed in this *** model combines batch data preprocessing method with knowledge *** increases the model to increase the model speed and maintains the original ***,double loss function is incorporated during the knowledge distillation process in this paper,so that the model can better learn the knowledge of the teacher model and the knowledge of the real *** accuracy of the model is *** effectiveness of the FasterBERT model is demonstrated on 12 datasets in this *** speed of the FasterBERT is 1.1-2.2 times higher than FastBERT and 2-43 times higher than *** addition,the accuracy is up to 0.6% higher than that of FastBERT,and the accuracy is better than that of the BERT model on three *** experiments show that the FasterBERT model proposed in this paper performs well on six Chinese datasets and six English *** speed is greatly improved while the accuracy of the task also has a significant effect.

关键词： pre-trained language model FasterBert model double loss function batch data processing knowledge distillation

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