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Charles L. Cooney and Gregory J. McRae. Engel, Morten Aleksandr, 1970- Massachusetts Institute of Technology. Dept. of Chemical Engineering. Massachusetts Institute of Technology. Department of Chemical Engineering 2005-08-22T22:49:00Z 2005-08-22T22:49:00Z 1999 1999 http://hdl.handle.net/1721.1/9109 45131424 Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 1999. Includes bibliographical references (leaves 253-261). A comprehensive hierarchical methodology has been developed to assist decision-makers allocate resources for experimentation during the initial-tages of pharmaceutical and chemical process development. The goal is to identify the most useful information that can be obtained for the least amount of effort and time. The allocation of resources for information gathering is based on Bayesian experimental design. Specifically, experimental designs for parameter estimation, model discrimination, and decisionmaking have been examined. Solving some of these design problems rigorously has not previously been attempted due to the mathematical complexity involved and sheer computational intensity of classical methods. The enabling technology is the use of polynomial chaos expansions to represent process and decisions models. A compact representation of uncertainty permits a rapid evaluation of expected values and variances in the decision models. In typical applications the computational burden was reduced by more than four orders of magnitude. The technique allows processes with industrial levels of complexity to be analyzed. The methodology takes a hierarchical approach. Initially the process subsystem that most adversely affects the objectives is identified. In this way resources are only allocated to studying the most important components. Metrics for measuring financial, environmental, and safety, objectives at different stages of the development process are suggested. The performance measures are unique to pharmaceutical and chemical manufacturing; however, the mathematical techniques developed are universally relevant. Examples showcase the experimental design approaches, the performance metrics, and the hierarchical modeling. A comprehensive case study, production of recombinant heparinase, highlights the most important aspects for an industrially relevant process. by Morten Aleksandr Engel. Ph.D. 261 leaves 18064521 bytes 18064280 bytes application/pdf application/pdf eng Massachusetts Institute of Technology M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 Chemical Engineering. Multiple objective resource allocation in product and process development Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="d381d676-f2c1-4d6e-b02c-bbbd14cd63af"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Multiple objective resource allocation in product and process development</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>1999</PublicationDate> <Authors> <Author> <DisplayName>Engel, Morten Aleksandr, 1970-</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Chemical Engineering.</Keyword> <Abstract>A comprehensive hierarchical methodology has been developed to assist decision-makers allocate resources for experimentation during the initial-tages of pharmaceutical and chemical process development. The goal is to identify the most useful information that can be obtained for the least amount of effort and time. The allocation of resources for information gathering is based on Bayesian experimental design. Specifically, experimental designs for parameter estimation, model discrimination, and decisionmaking have been examined. Solving some of these design problems rigorously has not previously been attempted due to the mathematical complexity involved and sheer computational intensity of classical methods. The enabling technology is the use of polynomial chaos expansions to represent process and decisions models. A compact representation of uncertainty permits a rapid evaluation of expected values and variances in the decision models. In typical applications the computational burden was reduced by more than four orders of magnitude. The technique allows processes with industrial levels of complexity to be analyzed. The methodology takes a hierarchical approach. Initially the process subsystem that most adversely affects the objectives is identified. In this way resources are only allocated to studying the most important components. Metrics for measuring financial, environmental, and safety, objectives at different stages of the development process are suggested. The performance measures are unique to pharmaceutical and chemical manufacturing; however, the mathematical techniques developed are universally relevant. Examples showcase the experimental design approaches, the performance metrics, and the hierarchical modeling. A comprehensive case study, production of recombinant heparinase, highlights the most important aspects for an industrially relevant process.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>