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Ability of in situ canopy spectroscopy to differentiate genotype by environment interaction in wheat

INTERNATIONAL JOURNAL OF REMOTE SENSING 2021年第10期42卷 3660-3680页

作者： Claudia, Arias Montero Bulacio, Enrique Nicolas, Rigalli Romagnoli, Martin Curin, Facundo Gonzalez, Fernanda G. Otegui, Maria E. Portapila, Margarita Univ Nacl Rosario Ctr Int Franco Argentino Ciencias Informat & Sist CONICET UNR Rosario Argentina CONICET UNNOBA CITNOBA Pergamino Argentina INTA Estn Expt Pergamino Buenos Aires DF Argentina Univ Buenos Aires Estn Expt Pergamino Fac Agron CONICET INTA FAUBA Buenos Aires DF Argentina

In recent years, the application of remote sensing techniques is gaining a growing interest and importance in agriculture. Researchers often combine data from near-infrared and red spectral bands according to their specific objectives. These types of combinations present the disadvantage of lack of sensitivity due to using a single or limited group of bands. In this work on-farm canopy spectral reflectance (CSR) data, composing of ten spectral bands (SBs) plus four spectral vegetation indices (SVIs), is considered in a joint manner to set up a methodology capable to identify genotype by environment interaction (GxE) in wheat. Spectral data are analysed over five wheat genotypes grown in five different environments. Historically breeders have recognized the potentially negative implications of GxE in selection and cultivar deployment and have focused on developing tools and resources to quantify it. We propose to perform a statistical batch processing, applying two-way analysis of variance to multiple spectral data, with genotype and environment as fixed factors. Results prove that this methodology performs well in both directions, capturing differences between genotypes within a single environment, and between environments for a single genotype, representing a step forward to converting spectral data into knowledge for the subject of GxE.

关键词： batch data processing

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Industrial data Science for batch Manufacturing Processes

arXiv

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arXiv 2022年

作者： Arzac-Garmendia, Imanol Vallerio, Mattia Perez-Galvan, Carlos Navarro-Brull, Francisco J. Department of Chemical Engineering KU Leuven Belgium SOLVAY SA Belgium Department of Chemical Engineering Imperial College London United Kingdom

batch processes show several sources of variability, from raw materials' properties to initial and evolving conditions that change during the different events in the manufacturing process. In this chapter, we will illustrate with an industrial example how to use machine learning to reduce this apparent excess of data while maintaining the relevant information for process engineers. Two common use cases will be presented: 1) AutoML analysis to quickly find correlations in batch process data, and 2) trajectory analysis to monitor and identify anomalous batches leading to process control improvements. © 2022, CC BY-NC-SA.

关键词： batch data processing

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In vitro and in vivo study of a colon-targeting resin microcapsule loading a novel prodrug, 3,4,5-tributyryl shikimic acid

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RSC ADVANCES 2016年第20期6卷 16882-16890页

作者： Dong, Kai Zeng, Aiguo Wang, Maoling Dong, Yalin Wang, Ke Guo, Chenning Yan, Yan Zhang, Lu Shi, Xianpeng Xing, Jianfeng Xi An Jiao Tong Univ Sch Pharm 76 Yanta West Rd Xian 710061 Shaanxi Peoples R China Shandong Univ Qilu Hosp Qingdao Shandong Peoples R China Xi An Jiao Tong Univ Coll Med Affiliated Hosp 1 Dept Pharm Xian 710061 Shaanxi Peoples R China

Prodrugs synthesized by different drugs not only overcome the defects of the original drugs, but also significantly enhance their treatment effects. In this study, a novel prodrug, 3,4,5-tributyryl shikimic acid (TBS), for the treatment of ulcerative colitis (UC) was synthesized by shikimic acid (SA) and butyric acid (BA) through the esterification reaction. Furthermore, the anion exchange resin, Amberlite 717 was employed to load the sodium salt of TBS through a batch process. Then the drug-loaded exchange resin (TBSS-IER) was encapsulated in the coating material, Eudragit S100, to prepare the colon-targeting drug resin microcapsule (TBSS-DRM) through an in-liquid drying method. The morphology and structure of TBSS-IER and TBSS-DRM were characterized by scanning electron microscopy (SEM). The in vitro release study demonstrated the good colon-targeting of TBSS-DRM. In the in vivo study, the TBSS-DRM exhibited a good therapeutic effect on the experimental colitis mouse induced by 2,4,6-trinitrobenzenesulfonic acid (TNBS). All results indicated that the prodrug was effective for colitis and the resin microcapsule system had good colon-targeting and could be used for the development of colon-targeting preparations.

关键词： batch data processing

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Sensor-guided micro assembly of active micro systems by using a hot melt based joining technology

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MICROSYSTEM TECHNOLOGIES-MICRO-AND NANOSYSTEMS-INFORMATION STORAGE AND processing SYSTEMS 2008年第12期14卷 1975-1981页

作者： Rathmann, Sven Schoettler, Kerstin Berndt, Michael Hemken, Gregor Raatz, Annika Tutsch, Rainer Boehm, Stefan Tech Univ Carolo Wilhelmina Braunschweig Inst Machine Tools & Prod Technol Braunschweig Germany Tech Univ Carolo Wilhelmina Braunschweig Inst Prod Measurement Technol Braunschweig Germany Tech Univ Carolo Wilhelmina Braunschweig Inst Joining & Welding Tech Braunschweig Germany

This article presents (the most) recent results of the subprojects B4 and B8 of the Collaborative Research Center 516-Design and Manufacturing of Active Micro Systems-which are concerned with the assembly of active micro systems. While subproject B4 investigates sensor guided assembly processes, subproject B8 develops suitable assembly techniques on the basis of non-viscous adhesive systems (hot melts). Process development focuses on the suitability for automation, process times and the applicability of batch processes. The article discusses certain hot melt application techniques that are suitable for batch production, a sensor-guided assembly system as well as different approaches for heat conduction in an automated assembly process for hot melt coated micro components.

关键词： batch data processing

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Sequential phase division by constructing pseudo time-slice for nonlinear multiphase batch process monitoring with uneven lengths

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IFAC-PapersOnLine 2017年第1期50卷 14070-14075页

作者： Zhang, Shumei Zhao, Chunhui State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China

batch and semi-batch processes are often characterized with strong nonlinearity. Most of the existing kernel-based methods proposed for nonlinear batch process monitoring have not considered the nonlinear characteristic, sequential phase division and uneven problem simultaneously. In this article, a novel similarity index is first defined on the basis of the kernel technique to describe the similarity of nonlinear characteristic in the high feature space. Then, the pseudo time-slice is constructed for each sample by searching samples within a range resembling to each time using the k-nearest neighbor (kNN) rule, which can effectively tackle the uneven problem without trajectory synchronization. A novel automatic sequential phase division procedure is proposed by analysing the nonlinear similarity between the local models derived from the pseudo time-slice and the representative model of each phase. For online application, the affiliation of each new sample is real-time judged to determine the proper phase model and fault status can be distinguished from phase shift event. To illustrate the feasibility and effectiveness, the proposed algorithm is applied to fed-batch penicillin fermentation process. © 2017

关键词： batch data processing Motion compensation Nearest neighbor search Process control Process monitoring K nearest neighbors kernel technique multiphase batches nonlinearity sequential phase division

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Improved batch Process Monitoring and Diagnosis Based on Multiphase KECA

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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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