Relevant bibliographies by topics / Batch optimization

Academic literature on the topic 'Batch optimization'

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Published: 14 March 2023

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Journal articles on the topic "Batch optimization"

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Paengjuntuek, Woranee, Paisan Kittisupakorn, and Amornchai Arpornwichanop. "Batch-to-batch Optimization of Batch Crystallization Processes." Chinese Journal of Chemical Engineering 16, no. 1 (February 2008): 26–29. http://dx.doi.org/10.1016/s1004-9541(08)60030-0.

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Lu, Pengcheng, Junghui Chen, and Lei Xie. "ILC Based Economic Batch-to-Batch Optimization for Batch Processes." IFAC-PapersOnLine 51, no. 18 (2018): 768–73. http://dx.doi.org/10.1016/j.ifacol.2018.09.270.

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Dong, Dong, Thomas J. McAvoy, and Evanghelos Zafiriou. "Batch to Batch Optimization Using Neural Network Models." IFAC Proceedings Volumes 29, no. 1 (June 1996): 6049–54. http://dx.doi.org/10.1016/s1474-6670(17)58650-4.

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Dong, Thomas J. McAvoy, and Evanghelos Zafiriou. "Batch-to-Batch Optimization Using Neural Network Models." Industrial & Engineering Chemistry Research 35, no. 7 (January 1996): 2269–76. http://dx.doi.org/10.1021/ie950518p.

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Kim, Boeun, Jakob K. Huusom, and Jay H. Lee. "Robust Batch-to-Batch Optimization with Scenario Adaptation." Industrial & Engineering Chemistry Research 58, no. 30 (April 17, 2019): 13664–74. http://dx.doi.org/10.1021/acs.iecr.8b06233.

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Srinivasan, B., S. Palanki, and D. Bonvin. "Dynamic optimization of batch processes." Computers & Chemical Engineering 27, no. 1 (January 2003): 1–26. http://dx.doi.org/10.1016/s0098-1354(02)00116-3.

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Srinivasan, B., D. Bonvin, E. Visser, and S. Palanki. "Dynamic optimization of batch processes." Computers & Chemical Engineering 27, no. 1 (January 2003): 27–44. http://dx.doi.org/10.1016/s0098-1354(02)00117-5.

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Gultekin, San, Avishek Saha, Adwait Ratnaparkhi, and John Paisley. "MBA: Mini-Batch AUC Optimization." IEEE Transactions on Neural Networks and Learning Systems 31, no. 12 (December 2020): 5561–74. http://dx.doi.org/10.1109/tnnls.2020.2969527.

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Efremenkov, V. V., and V. P. Chalov. "Optimization of glass batch preparation." Glass and Ceramics 57, no. 1-2 (January 2000): 37–39. http://dx.doi.org/10.1007/bf02681479.

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Choong, K. L., and R. Smith. "Optimization of batch cooling crystallization." Chemical Engineering Science 59, no. 2 (January 2004): 313–27. http://dx.doi.org/10.1016/j.ces.2003.09.025.

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Dissertations / Theses on the topic "Batch optimization"

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Mailhe, Maxime. "Batch processing task optimization." Thesis, Georgia Institute of Technology, 1994. http://hdl.handle.net/1853/11893.

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Terwiesch, Peter. "Dynamic optimization of batch process operations with imperfect modeling /." [S.l.] : [s.n.], 1994. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=10857.

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Aydin, Erdal [Verfasser]. "Tailored indirect algorithms for efficient on-line optimization of batch and semi-batch processes / Erdal Aydin." Magdeburg : Universitätsbibliothek, 2018. http://d-nb.info/1164498444/34.

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Moreno, Benito Marta. "Integrated batch process development based on mixed-logic dynamic optimization." Doctoral thesis, Universitat Politècnica de Catalunya, 2014. http://hdl.handle.net/10803/145068.

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Specialty chemicals industry relies on batch manufacturing, since it requires the frequent adaptation of production systems to market fluctuations. To be first in the market, batch industry requires decision-support systems for the rapid development and implementation of chemical processes. Moreover, the processes should be competitive to ensure their long-term viability. General-purpose and flexible plants and the consideration of physicochemical insights to define an efficient operation are also cornerstones for the success of specialty chemical industries. Precisely, this thesis tackles the systematic development of batch processes that are efficient, economically competitive, and environmentally friendly, to assist their agile introduction into production systems in grassroots and retrofit scenarios. Synthesis of conceptual processing schemes and plant allocation subproblems are solved simultaneously, taking into account the plant design. With this purpose, an optimization-based approach is proposed, where all structural alternatives are represented in a State-Equipment Network (SEN) superstructure, following formulated into a Mixed-Logic Dynamic Optimization (MLDO) problem which is later solved to minimize an objective function. Essentially, the strength of the proposed methodology lies in the modeling strategy which combines the different kinds of decisions of the integrated problem in a unique optimization model. Accordingly, it considers: (i) synthesis and allocation alternatives combination, (ii) dynamic process performance models and dynamic control variable profiles, (iii) discrete events associated to transitions of batch phases and operations, (iv) quantitative and qualitative information, (v) material transference synchronization to ensure batch integrity between unit procedures, and (vi) batch and semicontinuous processing elements. Different strategies can be used to solve the resulting MLDO problem. A deterministic direct-simultaneous approach is first proposed. The mixed-logic problem is reformulated into a mixed-integer one, which is fully-discretized to provide a Mixed-Integer Non-Linear Programming (MINLP) that is optimized using conventional solvers. Then, a Differential Genetic Algorithm (DGA) and a hybrid approach are presented. The purpose of these evolutionary strategies is to pose solution alternatives that keep solution goodness while seek for the improvement of computational efficiency to handle industrial-size problems. The optimization-based approach is applied in retrofit scenarios to solve the simultaneous process synthesis and plant allocation, taking into account the physical restrictions of existing plant elements. The production of specialty chemicals based on a competitive reactions system in an existing reactor network is first defined through process development and improvement according to different economic scenarios, decision criteria, and plant modifications. Additionally, a photo-Fenton process is optimized to eliminate an emergent wastewater pollutant in a given pilot plant, pursuing the minimization of processing time and cost. Batch process development in grassroots scenarios is also proven to be a problem of utmost importance to deal with uncertainty in future markets. Seeking for plant flexibility in several demand scenarios, the expected profit is maximized through a two-stage stochastic formulation that includes simultaneous plant design, process synthesis, and plant allocation decisions. A heuristic solution algorithm is used to handle the problem complexity. A grassroots plant design is defined to implement the previous competitive reaction system, where decisions like the feed-forward trajectories or operating modes allow the adaptation of master recipes to different demands. Finally, an acrylic fiber production example is presented to illustrate process development decisions like the selection of tasks, technological alternatives, chemicals, and solvent reuse.
La indústria de productes químics especials es basa en la fabricació discontinua, ja que permet adaptar de forma freqüent els sistemes de producció en funció de les fluctuacions de mercat. Per ser líder al sector, són necessàries eines de suport a la decisió que ajudin a l’àgil desenvolupament i implementació de nous processos. A més, aquests han de ser competitius per garantir la seva viabilitat a llarg termini. Altres peces clau per una operació eficient són l’ús de plantes flexibles així com l’estudi dels fenòmens fisicoquímics. Aquesta tesis aborda justament el desenvolupament sistemàtic de processos químics discontinus que siguin eficients, econòmicament competitius i ecològics, per contribuir a la seva ràpida introducció en els sistemes de producció, tant en escenaris de plantes existents com des de les bases. En concret, es planteja la resolució simultània de la síntesi conceptual d’esquemes de procés i l’assignació d’equips, tenint en compte el disseny de la planta. Amb aquest objectiu, es proposa una metodologia de solució basada en optimització, on les alternatives estructurals es representen en una Xarxa d’Estats i Equips (SEN per les sigles en anglès) que es formula mitjançant un problema d’Optimització Dinàmica Mixta-Lògica (MLDO per les sigles en anglès) que es resol minimitzant una funció objectiu. La solidesa de la metodologia proposada rau en la estratègia de modelat del problema MLDO, que integra els diferents tipus de decisions en un sol model d’optimització. En concret, es consideren: (i) la combinació d’alternatives de síntesi i assignació d’equips, (ii) models de procés i trajectòries de control dinàmics, (iii) esdeveniments discrets associats al canvi de fase i operació, (iv) informació quantitativa i qualitativa, (v) sincronització de transferències de material en tasques consecutives, i (vi) elements de processat discontinus i semi-continus. Existeixen diverses estratègies per resoldre el problema MLDO resultant. En aquesta tesi es proposa en primer lloc un mètode determinístic directe-simultani, on el model mixt-lògic es transforma en un mixt-enter. Aquest es discretitza al seu torn de forma completa per obtenir un problema de Programació No-Lineal Mixta-Entera (MINLP per les sigles en anglès) el qual es pot resoldre utilitzant algoritmes d’optimització convencionals. A més, es presenten un Algoritme Genètic Diferencial (DGA per les sigles en anglès) i un mètode híbrid. Totes dues estratègies esdevenen alternatives de cerca amb l’objectiu de mantenir la bondat de la solució i millorar l’eficàcia de computació per tractar problemes de dimensió industrial. La metodologia de solució proposada s’aplica al desenvolupament de processos discontinus en escenaris de plantes existents, tenint en compte les restriccions físiques dels equips. Un primer exemple aborda la manufactura de productes químics basada en un sistema de reaccions competitives. Concretament, es desenvolupa i millora el procés de producció implementat en una xarxa de reactors considerant diferents escenaris econòmics, criteris de decisió, i modificacions de planta. En un segon exemple, s’optimitza el procés foto-Fenton per ser executat en una planta pilot per eliminar contaminants emergents. Buscant integrar el desenvolupament de procés i el disseny de plantes flexibles en escenaris de base, es presenta una formulació estocàstica en dues etapes per a optimitzar el benefici esperat d’acord a diversos escenaris de demanda. Per gestionar la complexitat d’aquest problema es proposa la utilització d’una heurística. Com a exemple, es planteja el disseny d’una planta de base on implementar l’anterior sistema de reaccions competitives. Decisions com les trajectòries dinàmiques de control o la configuració d’equips permeten adaptar la recepta màster en funció de la demanda. Un darrer exemple defineix el procés de producció de fibra acrílica, il·lustrant decisions com la selecció de tasques, tecnologia, reactius o reutilització de dissolvents.
La industria productos químicos especiales se basa en la fabricación discontinua, la cual permite la adaptación frecuente de los sistemas de producción en función de las fluctuaciones de mercado. Para ser líder en el sector, son necesarias herramientas de soporte a la decisión que contribuyan al ágil desarrollo e implementación de nuevos procesos. Además, éstos deben ser competitivos para garantizar su viabilidad a largo plazo. Otras piezas clave para una operación eficiente son la utilización de plantas flexibles y el estudio de los fenómenos fisicoquímicos. Esta tesis aborda justamente el desarrollo sistemático de procesos químicos discontinuos que sean eficientes, económicamente competitivos y ecológicos, para contribuir a su rápida introducción en los sistemas de producción, ya sea en escenarios de plantas existentes o desde las bases. En particular, se plantea la resoluciónsimultánea de la síntesis conceptual de esquemas de proceso y la asignación de equipos, teniendo en cuenta además el diseño de planta.Con este fin, se propone una metodología de solución basada en optimización, donde todas las alternativas estructurales se representan en una Red de Estados y Equipos (SENpor sus siglas en inglés) que se formula mediante un problema de Optimización Dinámica Mixta-Lógica (MLDO por sus siglas en inglés) que se resuelve minimizando una función objetivo. La solidez de la metodología propuesta reside en la estrategia de modelado delproblema MLDO, que integra los diferentes tipos de decisiones en un solo modelo de optimización. En concreto, se consideran: (i) la combinación de alternativas de síntesis y asignación de equipos, (ii) modelos de proceso y trayectorias de control dinámicos, (iii)eventos discretos asociados al cambio de fase y operación, (iv) información cuantitativa y cualitativa, (v) sincronización de la transferencia de material en tareas consecutivas, y(vi) elementos de procesado discontinuos y semicontinuos.Existen diversas estrategias para resolver el problema MLDO resultante. En esta tesis se propone en primer lugar un método determinístico directo-simultáneo, donde el problema mixto-lógico se reformula en un mixto-entero. A su vez, éste se discretiza de formacompleta para obtener un problema de Programación No-Lineal Mixta-Entera (MINLP por sus siglas en inglés) el cual se puede resolver mediante algoritmos de optimización convencionales. Además, se presentan un Algoritmo Genético Diferencial (DGA por sussiglas en inglés) y un método híbrido. Ambas estrategias se plantean como alternativas de búsqueda con objeto de mantener la bondad de la solución y mejorar la eficacia de computación para tratar problemas de dimensión industrial.La metodología de solución propuesta se aplica al desarrollo de procesos discontinuos en escenarios con plantas existentes, teniendo en cuenta las restricciones físicas de los equipos. Un primer ejemplo aborda la fabricación de productos químicos basada en un sistema de reacciones competitivas. En concreto, se desarrolla y mejora el proceso de producción a implementar en una red de reactores considerando diferentes escenarios económicos, criterios de decisión, y modificaciones de planta. En un segundo ejemplo,se optimiza el proceso foto-Fenton a ser ejecutado en una planta piloto para eliminar contaminantes emergentes.Persiguiendo la integración del desarrollo de proceso con el diseño de plantas flexi-bles en escenarios base, se presenta asimismo una formulación estocástica en dos etapas para optimizar el beneficio esperado de acuerdo a varios escenarios de demanda. Paramanejar la complejidad de dicho problema se propone la utilización de una heurística.Como ejemplo, se plantea el diseño de una planta de base para implementar el anterior sistema de reacciones competitivas, donde decisiones como las trayectorias dinámicas de control o la configuración de equipos permiten adaptar la receta máster en función de lademandas. Por último, se presenta un ejemplo donde se define el proceso de producción de fibra acrílica, ilustrando decisiones como la selección de tareas, alternativas tecnológicas, reactivos químicos o la reutilización de disolventes.
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Dai, Jianbin. "Batch scheduling of two-machine limited-buffer flowshop with setup and removal times." Diss., Available online, Georgia Institute of Technology, 2004:, 2003. http://etd.gatech.edu/theses/available/etd-04062004-164623/unrestricted/dai%5Fjianbin%5F200312%5Fphd.pdf.

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Gao, Weihua [Verfasser]. "Isotherm Estimation and Batch Process Optimization for Preparative Chromatography / Weihua Gao." Aachen : Shaker, 2005. http://d-nb.info/1186587660/34.

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Yan, Lipeng. "The application of multivariate statistical analysis and optimization to batch processes." Thesis, University of Manchester, 2015. https://www.research.manchester.ac.uk/portal/en/theses/the-application-of-multivariate-statistical-analysis-and-optimization-to-batch-processes(e6dbe45d-94bb-4e84-a12f-542876af54f5).html.

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Multivariate statistical process control (MSPC) techniques play an important role in industrial batch process monitoring and control. This research illustrates the capabilities and limitations of existing MSPC technologies, with a particular focus on partial least squares (PLS).In modern industry, batch processes often operate over relatively large spaces, with many chemical and physical systems displaying nonlinear performance. However, the linear PLS model cannot predict nonlinear systems, and hence non-linear extensions to PLS may be required. The nonlinear PLS model can be divided into Type I and Type II nonlinear PLS models. In the Type I Nonlinear PLS method, the observed variables are appended with nonlinear transformations. In contrast to the Type I nonlinear PLS method, the Type II nonlinear PLS method assumes a nonlinear relationship within the latent variable structure of the model. Type I and Type II nonlinear multi-way PLS (MPLS) models were applied to predict the endpoint value of the product in a benchmark simulation of a penicillin batch fermentation process. By analysing and comparing linear MPLS, and Type I and Type II nonlinear MPLS models, the advantages and limitations of these methods were identified and summarized. Due to the limitations of Type I and II nonlinear PLS models, in this study, Neural Network PLS (NNPLS) was proposed and applied to predict the final product quality in the batch process. The application of the NNPLS method is presented with comparison to the linear PLS method, and to the Type I and Type II nonlinear PLS methods. Multi-way NNPLS was found to produce the most accurate results, having the added advantage that no a-priori information regarding the order of the dynamics was required. The NNPLS model was also able to identify nonlinear system dynamics in the batch process. Finally, NNPLS was applied to build the controller and the NNPLS method was combined with the endpoint control algorithm. The proposed controller was able to be used to keep the endpoint value of penicillin and biomass concentration at a set-point.
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Ibrahim, W. H. B. W. "Dynamic Modelling and Optimization of Polymerization Processes in Batch and Semi-batch Reactors. Dynamic Modelling and Optimization of Bulk Polymerization of Styrene, Solution Polymerization of MMA and Emulsion Copolymerization of Styrene and MMA in Batch and Semi-batch Reactors using Control Vector Parameterization Techniques." Thesis, University of Bradford, 2011. http://hdl.handle.net/10454/5392.

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Dynamic modelling and optimization of three different processes namely (a) bulk polymerization of styrene, (b) solution polymerization of methyl methacrylate (MMA) and (c) emulsion copolymerization of Styrene and MMA in batch and semi-batch reactors are the focus of this work. In this work, models are presented as sets of differential-algebraic equations describing the process. Different optimization problems such as (a) maximum conversion (Xn), (b) maximum number average molecular weight (Mn) and (c) minimum time to achieve the desired polymer molecular properties (defined as pre-specified values of monomer conversion and number average molecular weight) are formulated. Reactor temperature, jacket temperature, initial initiator concentration, monomer feed rate, initiator feed rate and surfactant feed rate are used as optimization variables in the optimization formulations. The dynamic optimization problems were converted into nonlinear programming problem using the CVP techniques which were solved using efficient SQP (Successive Quadratic Programming) method available within the gPROMS (general PROcess Modelling System) software. The process model used for bulk polystyrene polymerization in batch reactors, using 2, 2 azobisisobutyronitrile catalyst (AIBN) as initiator was improved by including the gel and glass effects. The results obtained from this work when compared with the previous study by other researcher which disregarded the gel and glass effect in their study which show that the batch time operation are significantly reduced while the amount of the initial initiator concentration required increases. Also, the termination rate constant decreases as the concentration of the mixture increases, resulting rapid monomer conversion. The process model used for solution polymerization of methyl methacrylate (MMA) in batch reactors, using AIBN as the initiator and Toluene as the solvent was improved by including the free volume theory to calculate the initiator efficiency, f. The effects of different f was examined and compared with previous work which used a constant value of f 0.53. The results of these studies show that initiator efficiency, f is not constant but decreases with the increase of monomer conversion along the process. The determination of optimal control trajectories for emulsion copolymerization of Styrene and MMA with the objective of maximizing the number average molecular weight (Mn) and overall conversion (Xn) were carried out in batch and semi-batch reactors. The initiator used in this work is Persulfate K2S2O8 and the surfactant is Sodium Dodecyl Sulfate (SDS). Reduction of the pre-batch time increases the Mn but decreases the conversion (Xn). The sooner the addition of monomer into the reactor, the earlier the growth of the polymer chain leading to higher Mn. Besides that, Mn also can be increased by decreasing the initial initiator concentration (Ci0). Less oligomeric radicals will be produced with low Ci0, leading to reduced polymerization loci thus lowering the overall conversion. On the other hand, increases of reaction temperature (Tr) will decrease the Mn since transfer coefficient is increased at higher Tr leading to increase of the monomeric radicals resulting in an increase in termination reaction.
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Yang, Ziqi. "Advanced batch process modelling, control and optimization for injection stretch blow moulding." Thesis, Queen's University Belfast, 2016. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.713464.

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The injection stretch blow Moulding (ISBM) process is widely used to manufacture PET bottles for the beverage and consumer goods industry. However, at present the ISBM process, including both the stretch blow moulding and reheating processes, are totally open-loop. The processes are complex and users often have to rely heavily on trial and error method to set up and control it. In this research, with the main purpose to displace this experience-based trial and error method in the ISBM process, the associated modelling and control methods are proposed.
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Jafartayari, Saman. "Modelling and Optimization of Batch Manufacturing Systems under Environmental and Economic Considerations." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/32264.

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Nowadays, minimization of the negative environmental impact of manufacturing processes is considered one of the most challenging problems in various industrial fields. Research communities and environmental legislators are continuously working to address these problems by placing significant efforts in devising new strategies to increase environmental sustainability. One of these problems is the lack of a comprehensive framework that can simultaneously improve economic aspects and lessen the impact on the environment. The need for a mathematical model that can assist firms in reaching suitable investment decisions has become of paramount importance. In this context, this study aims at optimizing the environmental and economic sustainability of batch production systems (i.e. a series of workstations where products are manufactured in batches). To this end, a profit maximization model was created by incorporating constraints such as budget, demand, greenhouse gas emissions and hazardous wastes within the manufacturing stage of product life cycle. Moreover, the model provides detailed guidelines on required improvements in a specific manufacturing system and calculates the investment associated with such implementations. This new approach was tested by using two different software packages and results were probed and discussed in different scenarios to investigate its validity. Sensitivity analysis and simulation results proved the consistency of the proposed mathematical model. In particular, in order to further assess the validity of the model, a pharmaceutical plant was selected as a case study, which also permitted discussion on additional aspects of the problem.
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Books on the topic "Batch optimization"

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Majozi, Thokozani. Batch chemical process integration: Analysis, synthesis and optimization. Dordrecht: Springer, 2010.

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Tieu, Doan. A dynamic optimization package and the application of an end-point collocation method to batch polymer reactors. Ottawa: National Library of Canada = Bibliothèque nationale du Canada, 1992.

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Majozi, Thokozani. Batch Chemical Process Integration: Analysis, Synthesis and Optimization. Springer, 2014.

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Majozi, Thokozani. Batch Chemical Process Integration. Springer, 2010.

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Majozi, Thokozani, Esmael Reshid Seid, and Jui-Yuan Lee, eds. Synthesis, Design, and Resource Optimization in Batch Chemical Plants. CRC Press, 2015. http://dx.doi.org/10.1201/b18200.

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Majozi, Thokozani, Jui-Yuan Lee, and Esmael Reshid Seid. Synthesis, Design, and Resource Optimization in Batch Chemical Plants. Taylor & Francis Group, 2015.

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Majozi, Thokozani, Jui-Yuan Lee, and Esmael Reshid Seid. Synthesis, Design, and Resource Optimization in Batch Chemical Plants. Taylor & Francis Group, 2015.

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Synthesis, Design, and Resource Optimization in Batch Chemical Plants. Taylor & Francis Group, 2017.

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synthesis, design, and resource optimization in batch chemical plants. CRC Press, 2015.

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Majozi, Thokozani, Jui-Yuan Lee, and Esmael Reshid Seid. Synthesis, Design, and Resource Optimization in Batch Chemical Plants. Taylor & Francis Group, 2015.

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Book chapters on the topic "Batch optimization"

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Westerberg, Arthur W. "Optimization." In Batch Processing Systems Engineering, 417–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-642-60972-5_20.

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Lange, Sascha, Thomas Gabel, and Martin Riedmiller. "Batch Reinforcement Learning." In Adaptation, Learning, and Optimization, 45–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27645-3_2.

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Feng, Xin, and Feifeng Zheng. "Integrated Job Scheduling with Parallel-Batch Processing and Batch Deliveries." In Combinatorial Optimization and Applications, 72–83. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03780-6_7.

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Pollack, Edward. "Batch Execution." In Analytics Optimization with Columnstore Indexes in Microsoft SQL Server, 97–110. Berkeley, CA: Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8048-5_7.

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Smalenberger, Kelly, and Michael Smalenberger. "Batch Bayesian Quadrature with Batch Updating Using Future Uncertainty Sampling." In Machine Learning, Optimization, and Data Science, 167–80. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-25599-1_13.

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Sarker, Ruhul, and Charles Newton. "Determination of Optimal Batch Size for a Manufacturing System." In Applied Optimization, 315–27. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4613-0301-5_21.

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Mockus, Jonas, William Eddy, Audris Mockus, Linas Mockus, and Gintaras Reklaitis. "Batch Process Scheduling Using Simulated Annealing." In Nonconvex Optimization and Its Applications, 245–59. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4757-2627-5_15.

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Bonvin, Dominique. "Control and Optimization of Batch Processes." In Encyclopedia of Systems and Control, 133–38. London: Springer London, 2015. http://dx.doi.org/10.1007/978-1-4471-5058-9_251.

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Bonvin, Dominique. "Control and Optimization of Batch Processes." In Encyclopedia of Systems and Control, 1–6. London: Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-5102-9_251-1.

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Costa, Ana Rita, Maria Elisa Rodrigues, Mariana Henriques, Rosário Oliveira, and Joana Azeredo. "Feed Optimization in Fed-Batch Culture." In Animal Cell Biotechnology, 105–16. Totowa, NJ: Humana Press, 2013. http://dx.doi.org/10.1007/978-1-62703-733-4_8.

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Conference papers on the topic "Batch optimization"

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Nguyen, Vu, Santu Rana, Sunil K. Gupta, Cheng Li, and Svetha Venkatesh. "Budgeted Batch Bayesian Optimization." In 2016 IEEE 16th International Conference on Data Mining (ICDM). IEEE, 2016. http://dx.doi.org/10.1109/icdm.2016.0144.

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Siivola, Eero, Akash Kumar Dhaka, Michael Riis Andersen, Javier Gonzalez, Pablo Garcia Moreno, and Aki Vehtari. "Preferential Batch Bayesian Optimization." In 2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2021. http://dx.doi.org/10.1109/mlsp52302.2021.9596494.

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Makarychev, Konstantin, Miklos Z. Racz, Cyrus Rashtchian, and Sergey Yekhanin. "Batch Optimization for DNA Synthesis." In 2021 IEEE International Symposium on Information Theory (ISIT). IEEE, 2021. http://dx.doi.org/10.1109/isit45174.2021.9517820.

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Sohn, Sungryull, Yinlam Chow, Jayden Ooi, Ofir Nachum, Honglak Lee, Ed Chi, and Craig Boutilier. "BRPO: Batch Residual Policy Optimization." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/391.

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In batch reinforcement learning (RL), one often constrains a learned policy to be close to the behavior (data-generating) policy, e.g., by constraining the learned action distribution to differ from the behavior policy by some maximum degree that is the same at each state. This can cause batch RL to be overly conservative, unable to exploit large policy changes at frequently-visited, high-confidence states without risking poor performance at sparsely-visited states. To remedy this, we propose residual policies, where the allowable deviation of the learned policy is state-action-dependent. We derive a new for RL method, BRPO, which learns both the policy and allowable deviation that jointly maximize a lower bound on policy performance. We show that BRPO achieves the state-of-the-art performance in a number of tasks.
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Clarke-Pringle, T. L., and J. F. MacGregor. "Optimization of molecular weight distribution using batch-to-batch adjustments." In Proceedings of the 1998 American Control Conference (ACC). IEEE, 1998. http://dx.doi.org/10.1109/acc.1998.703202.

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Zhang, Yanan, and Li Jia. "Particle Swarm Optimization based Optimization for Batch Processes." In 2019 Chinese Automation Congress (CAC). IEEE, 2019. http://dx.doi.org/10.1109/cac48633.2019.8996355.

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Shaohua Dong and He Sun. "Optimization of ERW pipe batch planning." In 2011 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC). IEEE, 2011. http://dx.doi.org/10.1109/aimsec.2011.6009873.

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Krothapally, M., and S. Palanki. "Online optimization of batch polymerization processes." In Proceedings of 16th American CONTROL Conference. IEEE, 1997. http://dx.doi.org/10.1109/acc.1997.609720.

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Maiti, Debadrita, Amiya K. Jana, Amar Nath Samanta, Swapan Paruya, Samarjit Kar, and Suchismita Roy. "Heat Integration in Batch Distillation Column." In INTERNATIONAL CONFERENCE ON MODELING, OPTIMIZATION, AND COMPUTING (ICMOS 20110). AIP, 2010. http://dx.doi.org/10.1063/1.3516310.

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Narayani, A., Munawar A. Shaik, Swapan Paruya, Samarjit Kar, and Suchismita Roy. "Reactive Scheduling in Multipurpose Batch Plants." In INTERNATIONAL CONFERENCE ON MODELING, OPTIMIZATION, AND COMPUTING (ICMOS 20110). AIP, 2010. http://dx.doi.org/10.1063/1.3516332.

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Reports on the topic "Batch optimization"

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Li, Tingwen, Dirk VanEssendelft, Justin Weber, Balaji Gopalan, Greggory Breault, Jonathan Tucker, and William Rogers. Validation and optimization of batch and continuous particle separation processes. Office of Scientific and Technical Information (OSTI), October 2018. http://dx.doi.org/10.2172/1479652.

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Miller, D., K. Fox, B. Pickenheim, and M. Stone. MELT RATE FURNACE TESTING FOR SLUDGE BATCH 5 FRIT OPTIMIZATION. Office of Scientific and Technical Information (OSTI), October 2008. http://dx.doi.org/10.2172/940390.

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Smith, L. B., and T. E. Jr Durney. Proof of concept and performance optimization of high gravity batch type centrifuge for dewatering fine coal. Office of Scientific and Technical Information (OSTI), July 1990. http://dx.doi.org/10.2172/6782968.

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Smith, L. B., and T. Durney. Proof of concept and performance optimization of high gravity batch-type centrifugal dryer for dewatering fine coal. Office of Scientific and Technical Information (OSTI), January 1991. http://dx.doi.org/10.2172/7233426.

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Smith, L. B., and T. Durney. Proof of concept and performance optimization of high gravity batch-type centrifugal dryer for dewatering fine coal. Final report, September 20, 1989--September 21, 1991. Office of Scientific and Technical Information (OSTI), December 1991. http://dx.doi.org/10.2172/10150192.

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Smith, L. B., and T. E. Jr Durney. Proof of concept and performance optimization of high gravity batch type centrifuge for dewatering fine coal. Quarterly technical progress report No. 3, March 20, 1990--June 20, 1990, Revision. Office of Scientific and Technical Information (OSTI), July 1990. http://dx.doi.org/10.2172/10126585.

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Matus, Sean, and Daniel Gambill. Automation of gridded HEC-HMS model development using Python : initial condition testing and calibration applications. Engineer Research and Development Center (U.S.), November 2022. http://dx.doi.org/10.21079/11681/46126.

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The US Army Corps of Engineers’s (USACE) Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) rainfall-runoff model is widely used within the research community to develop both event-based and continuous rainfall-runoff models. The soil moisture accounting (SMA) algorithm is commonly used for long-term simulations. Depending on the final model setup, 12 to 18 parameters are needed to characterize the modeled watershed’s canopy, surface, soil, and routing processes, all of which are potential calibration parameters. HEC-HMS includes optimization tools to facilitate model calibration, but only initial conditions (ICs) can be calibrated when using the gridded SMA algorithm. Calibrating a continuous SMA HEC-HMS model is an iterative process that can require hundreds of simulations, a time intensive process requiring automation. HEC-HMS is written in Java and is predominantly run through a graphical user interface (GUI). As such, conducting a long-term gridded SMA calibration is infeasible using the GUI. USACE Construction Engineering Research Laboratory (CERL) has written a workflow that utilizes the existing Jython application programming interface (API) to batch run HEC-HMS simulations with Python. The workflow allows for gridded SMA HEC-HMS model sensitivity and calibration analyses to be conducted in a timely manner.
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