Documents disponibles écrits par cet auteur

Dynamic simulation and decision support for multisite specialty chemicals supply chain / Arief Adhitya in Industrial & engineering chemistry research, Vol. 49 N° 20 (Octobre 2010) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 49 N° 20 (Octobre 2010) . - pp. 9917-9931 Titre : Dynamic simulation and decision support for multisite specialty chemicals supply chain Type de document : texte imprimé Auteurs : Arief Adhitya, Auteur ; Rajagopalan Srinivasan, Auteur Année de publication : 2011 Article en page(s) : pp. 9917-9931 Note générale : Chimie industrielle Langues : Anglais (eng) Mots-clés : Modeling  Dynamic  model Résumé : Companies are increasingly shifting from single-site manufacturing to multisite operations to tap into the vast business opportunities offered by globalization. The supply chain of such a multisite enterprise is complex, involving numerous interacting entities with various roles and constraints, resulting in complex dynamics and complexities in decision making. This complexity motivates the development of simulation models of the supply chain that can capture the behavior of the entities, their interactions, the resulting dynamics, and the various uncertainties. In this article, we present a dynamic model of a multisitc specialty chemicals supply chain that can serve as a quantitative simulation and decision support tool. The model explicitly considers the different supply chain entities and their interactions across various activities such as order acceptance and assignment, job scheduling, raw material procurement, storage, and production. It has been implemented as a dynamic simulator in Matlab/Simulink, called the integrated lube additive supply chain simulator (ILAS). Different policies, configurations, and uncertainties can be simulated in ILAS, and their impacts on the overall performance of the supply chain, such as customer satisfaction and profit, can be analyzed. The capabilities of ILAS for decision support are illustrated using several case studies. DEWEY : 660 ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=23325814 [article] Dynamic simulation and decision support for multisite specialty chemicals supply chain [texte imprimé] / Arief Adhitya, Auteur ; Rajagopalan Srinivasan, Auteur . - 2011 . - pp. 9917-9931. Chimie industrielle Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 49 N° 20 (Octobre 2010) . - pp. 9917-9931 Mots-clés : Modeling  Dynamic  model Résumé : Companies are increasingly shifting from single-site manufacturing to multisite operations to tap into the vast business opportunities offered by globalization. The supply chain of such a multisite enterprise is complex, involving numerous interacting entities with various roles and constraints, resulting in complex dynamics and complexities in decision making. This complexity motivates the development of simulation models of the supply chain that can capture the behavior of the entities, their interactions, the resulting dynamics, and the various uncertainties. In this article, we present a dynamic model of a multisitc specialty chemicals supply chain that can serve as a quantitative simulation and decision support tool. The model explicitly considers the different supply chain entities and their interactions across various activities such as order acceptance and assignment, job scheduling, raw material procurement, storage, and production. It has been implemented as a dynamic simulator in Matlab/Simulink, called the integrated lube additive supply chain simulator (ILAS). Different policies, configurations, and uncertainties can be simulated in ILAS, and their impacts on the overall performance of the supply chain, such as customer satisfaction and profit, can be analyzed. The capabilities of ILAS for decision support are illustrated using several case studies. DEWEY : 660 ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=23325814

Hierarchically distributed fault detection and identification through dempster–shafer evidence fusion / Kaushik Ghosh in Industrial & engineering chemistry research, Vol. 50 N° 15 (Août 2011) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 50 N° 15 (Août 2011) . - pp. 9249-9269 Titre : Hierarchically distributed fault detection and identification through dempster–shafer evidence fusion Type de document : texte imprimé Auteurs : Kaushik Ghosh, Auteur ; Sathish Natarajan, Auteur ; Rajagopalan Srinivasan, Auteur Année de publication : 2011 Article en page(s) : pp. 9249-9269 Note générale : Chimie industrielle Langues : Anglais (eng) Mots-clés : Failure  detection Résumé : Due to the sheer size and complexity of modem chemical processes, single centralized monolithic monitoring strategies are not always well suited for detecting and identifying faults. In this paper, we propose a framework for distributed fault detection and identification (FDI), wherein the process is decomposed hierarchically into sections and subsections based on a process flow diagram. Multiple hierarchical FDI methods at varying levels of granularity are deployed to monitor the various sections and subsections of the process. The results from the individual FDI methods contain mutually nonexclusive fault classes at different levels of granularity. We propose an adaptation of the Dempster-Shafer evidence theory to combine these diagnostic results at different levels of abstraction. The key benefits of this scheme as demonstrated through two case studies-a simulated CSTR-distillation column system and the Tennessee Eastman challenge process-are improved diagnostic performance compared to individual FDI methods, robust localization of even novel faults, and a coherent explanation of the entire plant's state. DEWEY : 660 ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=24395870 [article] Hierarchically distributed fault detection and identification through dempster–shafer evidence fusion [texte imprimé] / Kaushik Ghosh, Auteur ; Sathish Natarajan, Auteur ; Rajagopalan Srinivasan, Auteur . - 2011 . - pp. 9249-9269. Chimie industrielle Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 50 N° 15 (Août 2011) . - pp. 9249-9269 Mots-clés : Failure  detection Résumé : Due to the sheer size and complexity of modem chemical processes, single centralized monolithic monitoring strategies are not always well suited for detecting and identifying faults. In this paper, we propose a framework for distributed fault detection and identification (FDI), wherein the process is decomposed hierarchically into sections and subsections based on a process flow diagram. Multiple hierarchical FDI methods at varying levels of granularity are deployed to monitor the various sections and subsections of the process. The results from the individual FDI methods contain mutually nonexclusive fault classes at different levels of granularity. We propose an adaptation of the Dempster-Shafer evidence theory to combine these diagnostic results at different levels of abstraction. The key benefits of this scheme as demonstrated through two case studies-a simulated CSTR-distillation column system and the Tennessee Eastman challenge process-are improved diagnostic performance compared to individual FDI methods, robust localization of even novel faults, and a coherent explanation of the entire plant's state. DEWEY : 660 ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=24395870

Large - scale refinery crude oil scheduling by integrating graph representation and genetic algorithm / Manojkumar Ramteke in Industrial & engineering chemistry research, Vol. 51 N° 14 (Avril 2012) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 51 N° 14 (Avril 2012) . - pp. 5256-5272 Titre : Large - scale refinery crude oil scheduling by integrating graph representation and genetic algorithm Type de document : texte imprimé Auteurs : Manojkumar Ramteke, Auteur ; Rajagopalan Srinivasan, Auteur Année de publication : 2012 Article en page(s) : pp. 5256-5272 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Genetic  algorithm  Scheduling  Crude  oil  Refinery Résumé : Scheduling is widely studied in process systems engineering and is typically solved using mathematical programming. Although popular for many other optimization problems, evolutionary algorithms have not found wide applicability in such combinatorial optimization problems with large numbers of variables and constraints. Here we demonstrate that scheduling problems that involve a process network of units and streams have a graph structure which can be exploited to offer a sparse problem representation that enables efficient stochastic optimization. In the proposed structure adapted genetic algorithm, SAGA, only the subgraph of the process network that is active in any period is explicitly represented in the chromosome. This leads to a significant reduction in the representation, but additionally, most constraints can be enforced without the need for a penalty function. The resulting benefits in terms of improved search quality and computational performance are established by studying 24 different crude oil operations scheduling problems from the literature. ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=25783435 [article] Large - scale refinery crude oil scheduling by integrating graph representation and genetic algorithm [texte imprimé] / Manojkumar Ramteke, Auteur ; Rajagopalan Srinivasan, Auteur . - 2012 . - pp. 5256-5272. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 51 N° 14 (Avril 2012) . - pp. 5256-5272 Mots-clés : Genetic  algorithm  Scheduling  Crude  oil  Refinery Résumé : Scheduling is widely studied in process systems engineering and is typically solved using mathematical programming. Although popular for many other optimization problems, evolutionary algorithms have not found wide applicability in such combinatorial optimization problems with large numbers of variables and constraints. Here we demonstrate that scheduling problems that involve a process network of units and streams have a graph structure which can be exploited to offer a sparse problem representation that enables efficient stochastic optimization. In the proposed structure adapted genetic algorithm, SAGA, only the subgraph of the process network that is active in any period is explicitly represented in the chromosome. This leads to a significant reduction in the representation, but additionally, most constraints can be enforced without the need for a penalty function. The resulting benefits in terms of improved search quality and computational performance are established by studying 24 different crude oil operations scheduling problems from the literature. ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=25783435

Multivariate temporal data analysis using self-organizing maps. 1. Training methodology for effective visualization of multistate operations / Yew Seng Ng in Industrial & engineering chemistry research, Vol. 47 N°20 (Octobre 2008) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 47 N°20 (Octobre 2008) . - p. 7744-7757 Titre : Multivariate temporal data analysis using self-organizing maps. 1. Training methodology for effective visualization of multistate operations Type de document : texte imprimé Auteurs : Yew Seng Ng, Auteur ; Rajagopalan Srinivasan, Auteur Année de publication : 2008 Article en page(s) : p. 7744-7757 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : Self-organizing  map  (SOM)  Multivariate  temporal  data  analysis Résumé : Multistate operations are common in chemical plants and result in high-dimensional, multivariate, temporal data. In this two-part paper, we develop self-organizing map (SOM)-based approaches for visualizing and analyzing such data. In Part 1 of this paper, the SOM is used to reduce the dimensionality of the data and effectively visualize multistate operations in a three-dimensional map. A key characteristic of multistate processes is that the plant operates for long durations at steady states and undergoes brief transitions involving large changes in variable values. When classical SOM training algorithms are used on data from multistate processes, large portions of the SOM become dedicated to steady states, which exaggerates even minor noise in the data. Also, transitions are represented as discrete jumps on the SOM space, which makes it an ineffective tool for visualizing multistate operations. In this Part 1, we propose a new training strategy specifically targeted at multistate operations. In the proposed strategy, the training dataset is first resampled to yield equal representation of the different process states. The SOM is trained with this state-sampled dataset. Furthermore, clustering is applied to group neurons of high similarity into compact clusters. Through this strategy, modes and transitions of multistate operations are depicted differently, with process modes visualized as intuitive clusters and transitions as trajectories across the SOM. We illustrate the proposed strategy using two real-case studies, namely, startup of a laboratory-scale distillation unit and operation of a refinery hydrocracker. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie0710216 [article] Multivariate temporal data analysis using self-organizing maps. 1. Training methodology for effective visualization of multistate operations [texte imprimé] / Yew Seng Ng, Auteur ; Rajagopalan Srinivasan, Auteur . - 2008 . - p. 7744-7757. Chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 47 N°20 (Octobre 2008) . - p. 7744-7757 Mots-clés : Self-organizing  map  (SOM)  Multivariate  temporal  data  analysis Résumé : Multistate operations are common in chemical plants and result in high-dimensional, multivariate, temporal data. In this two-part paper, we develop self-organizing map (SOM)-based approaches for visualizing and analyzing such data. In Part 1 of this paper, the SOM is used to reduce the dimensionality of the data and effectively visualize multistate operations in a three-dimensional map. A key characteristic of multistate processes is that the plant operates for long durations at steady states and undergoes brief transitions involving large changes in variable values. When classical SOM training algorithms are used on data from multistate processes, large portions of the SOM become dedicated to steady states, which exaggerates even minor noise in the data. Also, transitions are represented as discrete jumps on the SOM space, which makes it an ineffective tool for visualizing multistate operations. In this Part 1, we propose a new training strategy specifically targeted at multistate operations. In the proposed strategy, the training dataset is first resampled to yield equal representation of the different process states. The SOM is trained with this state-sampled dataset. Furthermore, clustering is applied to group neurons of high similarity into compact clusters. Through this strategy, modes and transitions of multistate operations are depicted differently, with process modes visualized as intuitive clusters and transitions as trajectories across the SOM. We illustrate the proposed strategy using two real-case studies, namely, startup of a laboratory-scale distillation unit and operation of a refinery hydrocracker. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie0710216

Multivariate temporal data analysis using self-organizing maps. 2. Monitoring and diagnosis of multistate operations / Yew Seng Ng in Industrial & engineering chemistry research, Vol. 47 N°20 (Octobre 2008) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 47 N°20 (Octobre 2008) . - P. 7758-7771 Titre : Multivariate temporal data analysis using self-organizing maps. 2. Monitoring and diagnosis of multistate operations Type de document : texte imprimé Auteurs : Yew Seng Ng, Auteur ; Rajagopalan Srinivasan, Auteur Année de publication : 2008 Article en page(s) : P. 7758-7771 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : Self-organizing  map  (SOM)  Multivariate  temporal  data  analysis Résumé : The operation of transitions in continuous processes is challenging and often results in out-of-spec products, alarm floods, and abnormal situations. Therefore, efficient techniques for automated monitoring and fault diagnosis of such operations are essential. In Part 1 of this paper, we proposed a self-organizing map (SOM) training strategy for effectively visualizing multistate operations. In this part of the series, we use the same methodology as a representation scheme to compare operating trajectories and diagnosing faults during transient operations. In the proposed approach, clusters of SOM neurons, called neuronal clusters, serve as landmarks on the multivariate measurement space. Online data during the transition are reflected as a trajectory on the SOM and are converted to a sequence of neuronal clusters, which are the signature of the operating state. We have adapted the well-known Smith and Waterman discrete sequence comparison algorithm from bioinformatics to compare the state signatures and account for run-to-run variations. The proposed comparison method accounts explicitly for oscillations that are common in chemical processes. Online monitoring and diagnosis is performed by comparing the signature with those of known normal and abnormal transitions. The key advantage of the proposed strategy are its computational speed, inherent multivariate nature, and robustness to run-to-run variations, in addition to intuitiveness and visualization of the results. We illustrate the proposed method through two case studies: the Tennessee Eastman challenge problem and startup of a laboratory-scale distillation unit. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie071022y [article] Multivariate temporal data analysis using self-organizing maps. 2. Monitoring and diagnosis of multistate operations [texte imprimé] / Yew Seng Ng, Auteur ; Rajagopalan Srinivasan, Auteur . - 2008 . - P. 7758-7771. Chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 47 N°20 (Octobre 2008) . - P. 7758-7771 Mots-clés : Self-organizing  map  (SOM)  Multivariate  temporal  data  analysis Résumé : The operation of transitions in continuous processes is challenging and often results in out-of-spec products, alarm floods, and abnormal situations. Therefore, efficient techniques for automated monitoring and fault diagnosis of such operations are essential. In Part 1 of this paper, we proposed a self-organizing map (SOM) training strategy for effectively visualizing multistate operations. In this part of the series, we use the same methodology as a representation scheme to compare operating trajectories and diagnosing faults during transient operations. In the proposed approach, clusters of SOM neurons, called neuronal clusters, serve as landmarks on the multivariate measurement space. Online data during the transition are reflected as a trajectory on the SOM and are converted to a sequence of neuronal clusters, which are the signature of the operating state. We have adapted the well-known Smith and Waterman discrete sequence comparison algorithm from bioinformatics to compare the state signatures and account for run-to-run variations. The proposed comparison method accounts explicitly for oscillations that are common in chemical processes. Online monitoring and diagnosis is performed by comparing the signature with those of known normal and abnormal transitions. The key advantage of the proposed strategy are its computational speed, inherent multivariate nature, and robustness to run-to-run variations, in addition to intuitiveness and visualization of the results. We illustrate the proposed method through two case studies: the Tennessee Eastman challenge problem and startup of a laboratory-scale distillation unit. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie071022y

Robustness measures for operation schedules subject to disruptions / Badarinath Karri in Industrial & engineering chemistry research, Vol. 48 N° 20 (Octobre 2009) 

Permalink

Selection of Third-party service contracts for chemical logistics / Mukta Bansal in Industrial & engineering chemistry research, Vol. 47 n°21 (Novembre 2008) 

Permalink

Sequential methodology for scheduling of heat-integrated batch plants / Iskandar Halim in Industrial & engineering chemistry research, Vol. 48 N° 18 (Septembre 2009) 

Permalink

Supply chain redesign—multimodal optimization using a hybrid evolutionary algorithm / P. K. Naraharisetti in Industrial & engineering chemistry research, Vol. 48 N° 24 (Décembre 2009) 

Permalink

Supply chain redesign / P. K. Naraharisetti in Industrial & engineering chemistry research, Vol. 48 N° 24 (Décembre 2009) 

Permalink