Future WebCASTs are now being hosted through the APMonitor website. Please go to this site to see a list of upcoming talks as well as links to previous talks. If you have questions, please contact John Hedengren.
A profound change has taken place in the character of the chemical industry over the last 10-15 years. The process-centered emphasis has gradually shifted to a product-centered one. The implications are extensive and cover almost every aspect of a company's operations; process of invention, process of innovation, management of R&D, interactions with universities and technology development companies, capital allocation, and the character of business strategies and marketing efforts.
In addition, these trends have an effect on the educational preparation of scientists and engineers, a preparation that determines the educational and personal profiles of professional in the chemical industry.
In this presentation, I will discuss the development of the above shift and outline the essential characteristics of a product-centered chemical industry with particular emphasis on the following aspects:
Professor Stephanopoulos is the Arthur D. Little Professor of Chemical Engineering and Co-Director of the "Laboratory for Bioinformatics and Metabolic Engineering" at the Massachusetts Institute of Technology. He obtained his Ph.D. in chemical engineering from the University of Florida. His first academic appointment was at the University of Minnesota (1974). Subsequently he taught at the National Technical University of Athens (1981-84) and he joined MIT in 1984. During the period, July 2000 to July 2002, he was appointed as Chief Technology Officer of the Mitsubishi Chemical Corporation's group of companies, where he presently serves as Managing Director and member of the Board.
His past research interests have covered a broad spectrum of problems from product design, process development and design, to process operations analysis-diagnosis-planning and process control. His present research interests are in the areas of , (a) Bioinformatics (Functional Genomics and Metabolic Engineering), and (b) Multi-Scale Modeling of Materials and processes. His teaching interests have covered undergraduate/graduate subjects in Principles of Chemical Engineering, Thermodynamics, Process Design, Process Control, and Systems Engineering. He has authored, co-authored, and edited 10 books/monographs and over 190 journal papers.
He has been honored with the A.P. Colburn Award (AIChE), C. McGraw Award (ASEE), Dreyfus Teacher-Scholar Award, Computing in Chemical Engineering Award (AIChE-CAST Division), and the Best Paper Award in 1987 and 1992 (Computers and Chemical Engineering). In 1999 he was elected a member of the National Academy of Engineering, and in 2002 he received an Honorary Doctorate of Science from MacMaster University.
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Dec 2004 - Distributed Decision Making by Prof. B. Erik Ydstie
In this web-cast presentation I will give an overview of decision making and portfolio management in highly distributed organizations. The business system is modeled as a flexible network of semi-autonomous activities integrated with the market through service, cash and product transactions. There is a positive cost associated which each activity and transaction (the second law of thermodynamics), whereas cash is conserved (the first law of thermodynamics). These foundation principles allow us to develop a very rich topological structure for finance and business decision making based on cost, cash-flow and value analysis.
The objective of the business decision maker is to manage the activity rates and project portfolio so that the “intrinsic value” of the enterprise is maximized without incurring undue risk. According to Warren Buffet, the intrinsic value is the expected, discounted cash value of a project. A project can be a process modification, R&D resulting in a new product or process or a merger or acquisition. In this presentation I will explain how the dynamics of the network of integrated activities and project portfolio can be managed using distributed decision making and how good inventory and flow control reduce cost and risk. Risk is modeled as a Martingale process driven by stochastic fluctuations in the market. I also show that under certain conditions the intrinsic value is maximized by decentralized decision making and that the policies we generate leads to methodologies for decision making which are similar to those recently advocated by “lean manufacture”.
By making analogies between network thermodynamics and business decision making I show that the network paradigm leads to organizational structures that are agile and able to adapt to changing markets and new technologies. Industrial examples from automotive windshield production, solar grade silicon and aluminum production will be discussed.
Erik Ydstie is Professor of Chemical Engineering at Carnegie Mellon University. He received the B.Sc. and M.Sc. from the Norwegian Institute of Technology and his Ph.D. in 1982 from Imperial College. Professor Ydstie applies control theory to processes of practical interest to the chemical manufacturing industries.
The complete paper is published in Computers and Chemical Engineering, Vol 29/1 pp 11-27, Special issue: PSE 2003 - Edited by B. Chen and A. W. Westerberg.
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Crystalline organic solids are ubiquitous as either final products or as intermediates in the specialty chemical, pharmaceutical, and home & personal care industries. Virtually all small molecular weight drugs are isolated as crystalline materials, and over 90% of all pharmaceutical products are formulated in particulate, generally crystalline form. Crystalline chemical intermediates, such as adipic acid, are produced in large amounts to make polymers and specialty products. Skin creams and other personal care product formulations contain crystalline solids. In most cases the properties of the crystalline solid have a major impact on the functionality of the product as well as the design and operation of the manufacturing process.
Crystal size (or size distribution), shape, enantiomorph, and polymorph all influence product functionality. For example, even a 50 micron particle in a hand cream makes the cream feel gritty. Size distribution is important in the manufacture of beta-carotene, which is virtually insoluble in water and only sparingly soluble in vegetable oils, and is used as a food colorant. The color shade given to the food is determined by the narrow size distribution which must be in the submicron range. Crystal shape and polymorph influence solubility, dissolution rate (which influence bioavailability), compressibility (important for tabletting), and stability. The crystal enantiomorph is of vital importance in the manufacture of chiral materials, which has become a $100 billion/year industry in recent years. The choice of solvent, as well as the design and operation of the manufacturing process determine the crystal properties. Moreover, crystal size distribution, and shape have a major impact on the design of the manufacturing process since needle-like crystals or plate-like crystals can be difficult to filter and dry.
In this presentation we discuss the interactions between crystal engineering and crystallization process & product design. We assess the current status of knowledge in this field and identify critical areas for future research and development.
Michael F. Doherty is Professor of Chemical Engineering at the University of California, Santa Barbara. He received his B.Sc. in Chemical Engineering from Imperial College, University of London in 1973, and his Ph.D. in Chemical Engineering from Trinity College, University of Cambridge in 1977. He taught at the Universities of Minnesota and Massachusetts (where he reached the rank of University Distinguished Professor) before joining the faculty at UCSB. He has held a visiting appointment at the University of Minnesota in the Spring Quarter of 1981 and was a visiting scholar at the University of California at Berkeley for the 1984 calendar year. His research interests include design and synthesis of nonideal separation systems, separation with chemical reaction, and crystal engineering of organic materials. He is the holder of four patents, has published over 150 technical papers and delivered over 160 invited lectures; he was awarded best paper of the year in 1993 (jointly with M.F. Malone and Z.T. Fidkowski) and again in 2001 (jointly with M. F. Malone and S. B. Gadewar) by the editors of Computers and Chemical Engineering.
He is co-author of the textbook, Conceptual Design of Distillation Systems, McGraw-Hill (2001), and editor of the distillation chapters in Perry’s Handbook, and the Kirk-Othmer Encyclopedia of Chemical Technology. He has received numerous honors and awards for his teaching and research, including the Computing in Chemical Engineering Award of the CAST Division of the AIChE (awarded jointly with M. F. Malone in 1996), the Alpha Chi Sigma Award for Chemical Engineering Research of the AIChE (2004), the Clarence G. Gerhold Award of the Separations Division of the AIChE (2004) and the Excellence in Process Development Research Award of the Process Development Division of AIChE (awarded jointly with M. F. Malone in 2004). He has served as a consultant for many companies in the area of separations technology, and is a member of the Corporate Technical Advisory Boards for The Dow Chemical Company (2000-present) and Rhone–Poulenc (1997-1999).
At the University of Massachusetts Dr. Doherty was Head of Department from 1988-1997, and served as Director of the Center for Process Design and Control (1997-2000). He has been a Trustee of the CACHE Corporation since 1987 and served as its president from 2000-2002. In 1993 he was Chair of the Computing and Systems Technology Division of the AIChE. He serves as a member of the Editorial Boards for Computers and Chemical Engineering (1997-present), Process Systems Engineering Series, Academic Press (1997-present), Separation and Purification Methods (1998-2002), Industrial and Engineering Chemistry Research (1995-1998), and Transactions of the IChemE Part A: Chemical Engineering Research & Design (2001- 2003).
Oct 2005 - Advanced Process Control in Semiconductor Manufacturing by Prof. Costas Spanos and Dr. Thomas Sonderman
Semiconductor manufacturing error budgets are getting tighter and traditional metrology and control methods cannot keep up with the ever shrinking dimensions. In the sub-100nm generation of technologies, Critical Dimensions (CDs) are hard to measure, let alone control, since error budgets are almost consumed by measurement error. As a result, integrated circuit designers are called upon to produce designs that can tolerate large amounts of variability, and IC production facilities turn to more advanced process control schemes. This new reality challenges the historical modes of interaction between IC designers and producers. In this talk we will present this problem, and we will highlight present and future cutting edge applications in metrology, control, and design for manufacturability.
COSTAS J. SPANOS received the EE Diploma from the National Technical University of Athens, Greece and the M.S. and Ph.D. degrees in Electrical and Computer Engineering from Carnegie Mellon University. In 1988 he joined the faculty at the department of Electrical Engineering and Computer Sciences of the University of California at Berkeley, where he is now a Professor, and the Associate Dean for Research in the College of Engineering. Dr. Spanos was the editor of the IEEE Transactions on Semiconductor Manufacturing, and has published more than 130 referred publications.
His research interests include the development of flexible manufacturing systems, the application of statistical analysis in the design and fabrication of integrated circuits, and the development of novel sensors and computer-aided techniques. In 2000 he was elected Fellow of the IEEE for contributions and leadership in semiconductor manufacturing.
THOMAS SONDERMAN is the Director of Automated Precision Manufacturing (APM) Technology for AMD with global responsibility for the design, development and implementation of manufacturing technologies within AMD’s wafer fab and assembly operations.
Thomas has held numerous management and engineering positions during his 15-year tenure with AMD. Prior to joining AMD, Sonderman worked as a process control engineer for Monsanto Chemical Inc. He obtained a BS in Chemical Engineering from the University of Missouri in 1986 and a Masters degree in Electrical Engineering from National Technological University in 1991.
Thomas has a broad range of experience in the area of manufacturing automation and its application to high volume semiconductor fabrication. He is a highly sought-after speaker at industry conferences and is member of two advisory committees at the University of Texas: Chemical Engineering and Science, Technology and Society. Sonderman is the author of over 40 patents/patents pending and has published numerous articles in the area of automated control and manufacturing technology.
Most chemical engineers are (indirectly) trained to be “feedforward thinkers" and they immediately think of “model inversion'' when it comes doing control. Thus, they prefer to rely on models instead of data, although simple feedback solutions in many cases are much simpler and certainly more robust.
The seminar starts with a simple comparison of feedback and feedforward control and their sensitivity to uncertainty. Then two nice applications of feedback are considered:
Sigurd Skogestad was born in Norway in 1955. He received the Siv.Ing. degree (Diploma Engineer) in chemical engineering from the Norwegian University of Science and Technology (NTNU) in Trondheim in 1978. After finishing his military service at the Norwegian Defence Research Institute, he worked from 1980 to 1983 with Norsk Hydro in the areas of process design and simulation at their Reseach Center in Porsgrunn, Norway. He then spent 3.5 years in the US working towards his Ph.D. under the guidance of Manfred Morari, receiving the Ph.D. degree from the California Institute of Technology in 1987. He has been a professor of chemical engineering at the Norwegian University of Science and Technology (NTNU) since 1987, and since 1999 he is Head of Department of Chemical Engineering (Kjemisk prosessteknologi). He was at sabattical leave at the University of California at Berkeley in 1994-95, and at the University of California at Santa Barbara in 2001-02.
He has a group of about 10 Ph.D. students and is the Head of PROST which is the strong point center in process systems engineering in Trondheim and involves about 50 people in various departments.
The goal of his research is to develop simple yet rigorous methods to solve problems of engineering significance. Research interests include the use of feedback as a tool to (1) reduce uncertainty (including robust control), (2) change the system dynamics (including stabilization), and (3) generallly make the system more well-behaved (including self-optimizing control). Other interests include limitations on performance in linear systems, control structure design and plantwide control, interactions between process design and control, and distillation column design, control and dynamics.
The author of more than 100 journal publications and 150 conference publications, he is the principal author together with Ian Postlethwaite of the book "Multivariable feedback control" published by Wiley in 1996 (first edition) and 2005 (second edition). In October 2000 he published a book on "Process engineering - mass and energy balances" and a second edition came in August 2003 (In Norwegian; , Prosessteknikk (Tapir, 2000/2003) (he is considering writing an English edition.)
Dr. Skogestad was awarded "Innstilling to the King" for his Siv.Ing. degree in 1979, a Fullbright fellowship in 1983, received the Ted Peterson Award from AIChE in 1989, the George S. Axelby Outstanding Paper Award from IEEE in 1990, and the O. Hugo Schuck Best Paper Award from the American Automatic Control Council in 1992. He was an Editor of Automatica during the period 1996-2002.
In the autumn he teaches a course on introduction to process engineering based on his own text book. He used to teach the process control course for the 4th year students, but more recently this has been taken over by professor Heinz Preisig. Since 1989 he taught a Ph.D. course in robust multivariable control in the Control Department, based on his book with Ian Postlethwaite, but the course was given for the last time in spring 1999, and it has been replaced by an advanved undergraduate course given by Professor Morten Hovd. The engineering degree at NTNU has recently (first 5-year students graduated in 2002) changed from a 4.5 year program to a 5 year program and the siv.ing. degree is now considered equivalent to a M.Sc. degree. Professor Skogestad presentlty teaches a new advanced process control module for the 5th year students.
In this talk we will describe how optimization-based computational tools can be used to guide strain redesign leading to targeted overproductions. For example, production of bio-ethanol or complex molecules such as terpenes. Using as a starting point stoichiometric models of microbial metabolism, we will first explore how optimization can be used to pinpoint which new functionalities to add to the microbial host to endow it with new capabilities extracted from a generated database of more than 5,700 reactions. Building on this computational infrastructure, we will then present an integrated framework for identifying optimal microbial strain redesign strategies allowing for (i) additions, (ii) deletions and (iii) modulations (i.e., activations or inhibitions) of targeted reactions in the metabolic network. Finally we will explore how optimization can be used to analyze the topological properties of metabolic networks, identify pathway gaps and suggest ways of filling them. The developed computational tools will be highlighted using a number of design case-studies and the predictions will be contrasted with experimental results.
Costas D. Maranas (b. 1967), Donald B. Broughton Professor, Department of Chemical Engineering, The Pennsylvania State University, BS, Chemical Engineering, Aristotle University, Greece, (1990); MA, Chemical Engineering, Princeton University (1992); Ph.D. in Chemical Engineering, Princeton University (1995); Allan P. Colburn Award for Excellence in Publication (2002), Editorial Board for Biophysical Journal, Computers & Chemical Engineering, Journal of Global Optimization and Metabolic Engineering; Reviewer for NSF, NIH and DOE; Research interests: Modeling and optimization of directed evolution protocols for protein engineering, analysis and optimization of metabolic and signaling networks, optimal design of biological circuits and synthetic biology, inference of gene regulatory networks, real options based optimization of product and R&D pipelines, optimization theory and algorithms.
This talk describes applications of molecular simulation to chemical reacting systems and the subsequent development of techniques for multiscale simulation and multiscale systems engineering. The progression of applications of simulation from macroscopic to molecular to multiscale is reviewed. Multiscale systems are presented as an approach that incorporates molecular and multiscale simulation to design processes that control events at the molecular scale while simultaneously optimizing all length scales from the molecular to the macroscopic. It is discussed how multiscale modeling and the targeted design of processes and products at the molecular scale can be addressed using the multiscale systems tools. In addition to addressing challenging problems in materials, microelectronics, and biotechnology, this provides a framework for addressing the “grand challenge” of nanotechnology: how to move nanoscale science and technology from art to an engineering discipline.
Richard Braatz is Professor and Millennium Chair of Chemical and Biomolecular Engineering at the University of Illinois at Urbana-Champaign. Before starting at U of I he received M.S. & Ph.D. degrees from Caltech and spent a year at DuPont. Dr. Braatz is a co-author of ~100 journal papers and 3 books, and has consulted and/or collaborated with more than 10 companies including Merck, IBM, and UTC Fuel Cells. Honors and awards include the AACC Donald P. Eckman Award (2000), the ASEE Curtis W. McGraw Research Award (2004), the AIChE CAST Outstanding Young Researcher Award (2005), and the IEEE Antonio Ruberti Young Researcher Prize (2005). Dr. Braatz’s main research interests are in modeling, design, and control of complex and multiscale systems, with applications in microelectronics, pharmaceuticals, and biotechnology.
This course will attempt to give the audience a general overview on theories and practice of model predictive control and system identification. Fundamental theories as well as current industrial algorithms and practice will be covered. A short discussion on various system identification methods that are being used to build models for MPC will also be given.
Dr. Lee is currently Director of the Integrated Sensing, System Identification, and Control Laboratory (ISSICL). His group is working on ways to use powerful computers, numerical optimization methods, information processing techniques, and novel sensors to improve the safety and efficiency of chemical and biological processes. The cornerstone of their research is a computer-based optimal control technique called Model Predictive Control (MPC), which has already seen applications on many industrial processes (>3000 worldwide applications) with some impressive results. The main components of MPC are the model, the sensors, and the optimal control algorithm. His research group focuses on integration - rather than mere enhancement of the individual components of MPC. They are developing modeling and system identification tools that allow the user to tailor the modeling efforts to specific end-goals of the control. They are developing techniques for integrating several different types of sensors and a process model so that accurate predictions can be made about the whole system including the behavior of those variables that cannot be measured as frequently or reliably as desired. They are also developing smart control algorithms that make optimal decisions while fully accounting for uncertainties in the model and sensed information. They are conducting a number of fundamental studies on data-assisted modeling, sensing, and control, which are designed to improve the integration step. In addition to the fundamental studies, they are conducting in parallel several application studies involving challenging industrial process control problems, including those that arise in particulate processes, mammalian cell reactors, polymer reactors, simulated moving bed separation systems, and pulp and paper processes.
Dr. Lee received the National Science Foundation’s Young Investigator Award and a number of other research and teaching awards. He is also a co-author of the forthcoming book “Model Predictive Control.” He is a member of AIChE, IEEE, and ASEE, and participated in organizing several international conferences.
The webcast develops and discusses nonlinear programming (NLP) strategies for the optimization of nonlinear dynamic models that arise in both off-line and on-line applications in chemical process engineering. In particular, Dynamic Real-Time Optimization can play a significant part in the decision-making hierarchy that includes logistics, planning, scheduling and control. Its basic components deal with estimation of the system and identification of a system model, optimization of a system model and regulation to reject disturbances. Moreover, the inclusion of a consistent set of nonlinear process models is essential in order to coordinate optimization decisions made at different levels in the hierarchy.
The webcast briefly presents and summarizes nonlinear programming methods for dynamic optimization. In particular, it discusses simultaneous NLP formulations along with large-scale NLP solvers for dynamic optimization and demonstrates its effectiveness with real-world examples. Also described is the extension of this approach to nonlinear model predictive control (NMPC). In the last few years, these have emerged as efficient and reliable on-line NMPC strategies.
Finally, the webcast discusses the integration of dynamic models for off-line optimization to on-line model predictive control (MPC). In particular, we will discuss a fast sensitivity-based nonlinear MPC strategy that is not only consistent with rigorous off-line dynamic optimization models but requires very little on-line computation. A similar strategy will also be presented for moving horizon estimation with nonlinear models. All of these concepts will be illustrated with several case studies drawn from process engineering.
Professor Larry Biegler is the Bayer Professor of Chemical Engineering at Carnegie Mellon University. He received a BS degree from Illinois Institute of Technology and MS and PhD degrees from University of Wisconsin, Madison, all in chemical engineering. Prof. Biegler's research projects are in the areas of design research and systems analysis. His research centers on the development and application of concepts in optimization theory, operations research, and numerical methods for process design, analysis, and control. He has received numerous honors and awards including the Presidential Young Investigator Award from the National Science Foundation, the Curtis McGraw Research Award from the American Society for Engineering Education, and the Computing in Chemical Engineering Award from the CAST Division of the American Institute of Chemical Engineers.
The justification of process control in the context of business decision-making may include the following economic or operating considerations: increased product throughput, increased yield of higher valued products, decreased energy consumption, decreased pollution, decreased off-specification product, improved safety, extended life of equipment, improved operability, and decreased production labor. However, identifying a direct relationship between each type of economic benefit (profitability) and how controllers are designed or operated (controllability) is an elusive target. Perspectives of how process control has influenced business decision-making have changed radically over the brief history of process control (1950 to the present). Thus it is valuable to have an historical view of the changing role of process control in operations and profit/loss measures. Today the influence of process control on business decision-making is at its highest level ever, but there are still many challenges that must be met for process control to maximize its economic impact on an enterprise-wide scale. The opportunity to connect controllability to profitability appears greater for batch processing than for continuous processing.
Thomas F. Edgar is Professor of Chemical Engineering at the University of Texas at Austin and holds the George T. and Gladys Abell Chair in Engineering. Dr. Edgar received his B.S. in chemical engineering from the University of Kansas and a Ph.D. from Princeton University. For the past 35 years, he has concentrated his academic work in process modeling, control, and optimization, with over 200 articles and book chapters. Edgar has co-authored leading textbooks: Optimization of Chemical Processes (McGraw-Hill, 2001) and Process Dynamics and Control (Wiley, 2004). He has received major awards from AIChE (Colburn, Computing in Chemical Engineering, Lewis) and ASEE (Chemical Engineering Division, Westinghouse, and Meriam-Wiley). Recently he has carried out modeling and control research projects jointly with a variety of companies in the process industries under the auspices of the Texas-Wisconsin-California Control Consortium (www.che.utexas.edu/twmcc).
Multiscale simulation is emerging as a new scientific field in chemical, materials, and biological sciences. The idea of multiscale modeling is straightforward: one computes information at a smaller (finer) scale and passes it to a model at a larger (coarser) scale by leaving out degrees of freedom as one moves from finer to coarser scales. The obvious goal of multiscale modeling is to predict macroscopic behavior of an engineering process from first principles (bottom-up approach). However, the emerging fields of nanotechnology and biotechnology impose new challenges and opportunities (top-down). For example, the miniaturization of microchemical systems for portable and distributed power generation imposes new challenges and opportunities than the conventional scaling up chemical engineers have worked on. In this talk, I will describe the development of multiscale models for catalytic reactors with a focus on small-scale hydrogen production. Limitations in model development, including multi-level uncertainty, will be discussed. A new multiscale and informatics-based framework will be presented for design of experiments (DOE) in order to enable model assessment and parameter refinement. The framework is designed to overcome uncertainties by allowing experimental data injection into multiscale models. Finally, I will discuss how one could use these models to enable both catalyst design and microsystem optimization for portable and distributed power generation.
Dion Vlachos is a Professor at the Department of Chemical Engineering at the University of Delaware since 2003. He is currently an associate director of the Center for Catalytic Science and Technology. Dion obtained a five years diploma in Chemical Engineering from the National Technical Univ. of Athens, in Greece, in 1987. He obtained his MS and Ph.D. from the University of Minnesota in 1990 and 1992, respectively, and spent a postdoctoral year at the Army High Performance Computing Research Center, MN, after which he joined UMass as an Assistant Professor. He was promoted to an associate professor at UMass in 1998. Dion was a Visiting Fellow at Princeton University in the spring of 2000, a visiting faculty at Thomas Jefferson Univ. and Hospital in spring of 2007 and the George Pierce Distinguished Prof. of Chemical Engineering and Materials Science at the Univ. of Minnesota in the fall of 2007. Dion is the recipient of an ONR Young Investigator Award, a NSF Career Award, a Junior Faculty Award, and the Best Advisor Award (twice). He is a member of the American Institute of Chemical Engineers, American Chemical Society, The Combustion Institute, The Catalysis Society, and SIAM. His main research thrust is multiscale modeling and simulation along with their application to catalysis and portable microchemical devices for power generation, nucleation and growth of nanomaterials, microporous thin films, and molecular cell biology. He is the corresponding author of more than 160 refereed publications and has given more than 120 invited talks (plenary lectures, keynote lectures, etc.).
Linear model predictive control (LMPC) is well established as the industry standard for controlling constrained multivariable processes. A major limitation of LMPC is that plant behavior is described by linear dynamic models. As a result, LMPC is inadequate for highly nonlinear processes and moderately nonlinear processes with large operating regimes. This shortcoming coupled with increasingly stringent demands on throughput and product quality spurred the development of nonlinear model predictive control (NMPC). NMPC is conceptually similar to its linear counterpart except that nonlinear dynamic models are used for process prediction and optimization. The purpose of this WebCAST is to provide an overview of current NMPC technology with a focus on the real-time implementation issues required to obtain a computationally efficient controller. The necessary background will be introduced by reviewing basic concepts of nonlinear process modeling and optimization. The principles will be illustrated with a highly nonlinear cryogenic air separation column model.
Dr. Michael A. Henson is a Professor of Chemical Engineering and Director of the Process Design and Control Center at the University of Massachusetts Amherst. He received his B.S from the University of Colorado (1985), M.S from the University of Texas (1988), and Ph.D. from the University of California, Santa Barbara (1991), all in Chemical Engineering. Prior to his appointment at UMass, he held a faculty appointment at Louisiana State University and visiting positions at DuPont and the University of Stuttgart. He serves as an Associate Editor for Automatica and the Journal of Process Control. He has received several awards including the Career Development Award from the National Science Foundation and the Alexander von Humboldt Research Fellowship (Germany). His research interests are nonlinear modeling and control of complex chemical and biological systems.
Natural gas contributes roughly 20% of world energy consumption. Natural gas reserves are plentiful and natural gas produces less CO2 per unit of energy generated than any other hydrocarbon. The liquefied natural gas (LNG) segment is growing very rapidly and is enabling the emergence of a global natural gas market. Natural gas value chains have very distinctive features arising from the low volumetric energy density of natural gas, and the significance of gas quality and pressure in supply chain operations. Gas infrastructure investments can be risky due to the high capital and specificity of the infrastructure, leading to complex ownership and contractual agreements amongst multiple parties to manage this risk. This talk will present two case studies applying optimization formulations to the design and operation of natural gas value chains. Short-term operational planning in upstream natural gas supply chains can play an important role in ensuring reliable supplies, consistent fulfillment of customer requirements and efficient management of production and transportation infrastructure. A real world case study involving the Sarawak Gas Production System (SGPS), located in East Malaysia and operated by Sarawak Shell, is presented to demonstrate a short-term (8-12 weeks) production allocation model and optimization framework for the upstream natural gas supply chain. The second case study involves the design of a novel liquefied energy chain for the exploitation of remote offshore natural gas combined with CO2 capture and sequestration with enhanced oil recovery. Here optimization is used to design novel offshore and onshore subambient processes required to implement the proposed supply chain.
Paul Barton is the Lammot du Pont Professor of Chemical Engineering at MIT, where he has been since 1992. He received his Ph.D. from the Centre for Process Systems Engineering at Imperial College, London University in 1992. He has held Visiting Professor appointments at CNRS-ENSIC, Nancy, France and EPFL, Lausanne, Switzerland. He has industrial experience with BP and Air Products, and has consulted for major corporations including Dow Chemical, Alstom Power and Aspen Technology. In 2004 he was awarded the Outstanding Young Researcher Award by AIChE's CAST Division. Barton's research interests include hybrid discrete/continuous dynamic systems; numerical analysis of ordinary differential, differential-algebraic and partial differential-algebraic equations; sensitivity analysis and automatic differentiation; global, mixed-integer and dynamic optimization theory and algorithms; and open process modeling software. Some of the applications his group is currently focusing on include energy systems engineering, continuous pharmaceutical manufacturing and organic electronic devices. He served as Director of AIChE's CAST Division from 2001-2004 and is currently a subject editor for the journal /Optimal Control Applications and Methods/. He is author or co-author of over 80 articles in refereed journals. He has been very active in the design and the development of process modeling software, having been the original author of gPROMS, and having led the development of ABACUSS/JACOBIAN and DAEPACK at MIT, all of which are now commercial products widely used in industry.
Type 1 diabetes mellitus is a disease characterized by complete pancreatic beta-cell insufficiency. The only treatment is with subcutaneous or intravenous insulin injections, traditionally administered in an open-loop manner. Patients attempt to mimic normal physiology in order to prevent the complications of hyper- and hypoglycemia (elevated glucose levels, and low glucose levels, respectively). Even minor glucose elevations increase the risk of complications (retinopathy, nephropathy, and peripheral vascular disease).
In recent years, sensors and pumps have become available that show great promise for a closed-loop artificial pancreas -- however the crucial missing component is the algorithm to connect the devices. In order to normalize the glucose levels of insulin dependent, type 1 diabetic patients, the algorithms for the development of an artificial pancreatic islet need to exploit all the measured variables that the normal islet insulin secretion utilizes and quickly increase or decrease the insulin secretory.
Our group has been working on model-based control algorithms for pump control over the last 17 years; with clinical evaluations over the last 7 years in collaboration with the Sansum Diabetes Research Institute. Our investigations have addressed the critical algorithmic elements of: model identification, disturbance estimation, model predictive controller design, event detection, monitoring & alarming, and optimization solution. In this talk, we present our most recent computational and clinical results in pursuit of the artificial beta cell. Our novel contributions include the model formulation, meal detection & estimation schemes, efficient programming formulation, and the use of insulin-on-board constraints to ensure safety.
Dr. FRANCIS J. DOYLE III is the Associate Dean for Research in the College of Engineering at UC, Santa Barbara and he is the Associate Director of the Army Institute for Collaborative Biotechnologies. He holds the Duncan and Suzanne Mellichamp Chair in Process Control in the Department of Chemical Engineering, as well as appointments in the Electrical Engineering Department, and the Biomolecular Science and Engineering Program. He received his B.S.E. from Princeton (1985), C.P.G.S. from Cambridge (1986), and Ph.D. from Caltech (1991), all in Chemical Engineering. Prior to his appointment at UCSB, he has held faculty appointments at Purdue University and the University of Delaware, and held visiting positions at DuPont, Weyerhaeuser, and Stuttgart University. He is the recipient of several research awards (including the NSF National Young Investigator, ONR Young Investigator, Humboldt Research Fellowship) as well as teaching awards (including the Purdue Potter Award, and the ASEE Ray Fahien Award). He is currently the editor-in-chief of the IEEE Transactions on Control Systems Technology, and holds Associate Editor positions with the Journal of Process Control, the SIAM Journal on Applied Dynamical Systems, and Royal Society’s Interface. In 2005, he was awarded the Computing in Chemical Engineering Award from the American Institute of Chemical Engineers for his innovative work in systems biology. His research interests are in systems biology, network science, modeling and analysis of circadian rhythms, drug delivery for diabetes, model-based control, and control of particulate processes.
Enterprise-wide optimization (EWO) has become a major goal in the process industries due to the increasing pressures for remaining competitive in the global marketplace. EWO involves optimizing the operations of supply, manufacturing and distribution activities of a company to reduce costs, inventories and environmental impact, and to maximize profits and responsiveness. Major operational items include planning, scheduling, real-time optimization and control. We provide an overview of EWO in terms of a mathematical programming framework. We first provide a brief overview of mathematical programming techniques (mixed-integer linear and nonlinear optimization methods), as well as decomposition methods, stochastic programming and modeling systems. We then address some of the major issues involved in the modeling and solution of these problems. Based on the EWO program at the Center of Advanced Process Decision-making at Carnegie Mellon, we show the scope of these models by describing several applications that include optimal refinery planning, simultaneous planning and scheduling in multisite facilities for continuous multiproduct plants, optimization of industrial gas distribution systems, design and planning under uncertainty of offshore oil field infrastructures, and optimization of responsive supply chain design and planning with demand uncertainty. Finally, we close with a brief discussion of future directions of research in the EWO area.
Prof. Ignacio E. Grossmann is the Rudolph R. and Florence Dean University Professor of Chemical Engineering, and former Department Head at Carnegie Mellon University. He obtained his B.S. degree in Chemical Engineering at the Universidad Iberoamericana, Mexico City, in 1974, and his M.S. and Ph.D. in Chemical Engineering at Imperial College in 1975 and 1977, respectively. After working as an R&D engineer at the Instituto Mexicano del Petróleo in 1978, he joined Carnegie Mellon in 1979. He was Director of the Synthesis Laboratory from the Engineering Design Research Center in 1988-93. He is director of the "Center for Advanced Process Decision-making" which comprises a total of 20 petroleum, chemical and engineering companies. Ignacio Grossmann is a member of the National Academy of Engineering, Mexican Academy of Engineering, and associate editor of AIChE Journal and member of editorial board of Computers and Chemical Enginering, Journal of Global Optimization, Optimization and Engineering, Latin American Applied Research, and Process Systems Engineering Series. He was Chair of the Computers and Systems Technology Division of AIChE , and co-chair of the 1989 Foundations of Computer-Aided Process Design Conference and 2003 Foundations of Computer-Aided Process Operations Conference. He is a member of the American Institute of Chemical Engineers, Sigma Xi, Institute for Operations Research and Management Science, and American Chemical Society.
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Chemical products such as fuels, coatings, lubricants and cosmetics must be designed to meet specific customer, environmental, safety and regulatory constraints. Designing such products is a combinatorial problem that can involve searching through thousands of candidate molecular structures and mixture formulations. In years past, this search was conducted primarily experimentally, a costly and time-consuming process. Today, with a global marketplace demanding an ever-greater rate of product innovation, new approaches to chemical product design are needed. This course will discuss what chemical product design is, explain why it is important for chemical engineers to understand it, and present approaches and examples of how chemical product design is done.
Michael Hill is Lecturer in Chemical Engineering Design at Columbia University in New York City. He obtained his BS and MS degrees in Chemical Engineering from Columbia University, and subsequently joined Unilever Research in Edgewater, NJ in 1983. Michael remained with Unilever for 22 years, leading R&D departments in various product categories, most notably Skin Care and Cleansing. Michael also spent 3 years in the Unilever Research Port Sunlight Laboratory in the U.K. He left Unilever in early 2005, and has been teaching Chemical Product and Process Design at Columbia University ever since. In addition to writing numerous internal Unilever Research documents, Michael has authored several papers and book chapters on various aspects of chemical product and process design. He is Chair of AIChE’s Process Development Division, and has been a Fellow of AIChE since 2008.
Kevin G. Joback is president of Molecular Knowledge Systems. For more than 25 years Kevin has worked in the areas of physical property estimation and chemical product design. He has developed a number of group contribution estimation techniques now widely used in industry. He has designed numerous chemical products including environmentally friendly cleaning and separation solvents, new lubricants, enhanced thermal storage materials, improved jet and rocket fuels, and non-hazardous aircraft deicing fluids. Kevin holds a bachelor’s degree from Stevens Institute of Technology and a Masters and PhD from MIT, all in chemical engineering.
The pharmaceutical industry is a large, high value added manufacturing sector, with annual worldwide sales of nearly $1 trillion. The traditional manufacturing mode in this sector has been batch operation. However, recent advances in technologies, changes in the regulatory climate and continuously drivers for cost reduction have provided a unique opportunity for the introduction of advanced manufacturing technologies.
Continuous processing is considered to be one of the key technologies that can provide significant innovation in the pharmaceutical sector also motivated by the vision of developing “on demand” personalised medicines. In addition to offering better product consistency, and overall process efficiency, continuous manufacturing has the potential to provide more distributed and even mobile manufacturing systems that could be located at the point of use, improving access to novel medicines, opening new market opportunities, reducing costs, driving innovation and speeding time to market. However, to be able to exploit the advantages of continuous manufacturing processes in an industry characterised by high value, high variety and low volume products obtained through a network of distributed manufacturing systems, advances are required in fundamental process understanding, continuous processing and equipment in particular for chemical solids and in measurement, modeling and control methodologies.
The presentation will provide an overview of the advantages and challenges, including regulatory aspects, related to the continuous manufacturing of pharmaceuticals, from synthesis to formulation of the final product. We will provide a brief overview of aspects related to continuous production of active pharmaceutical ingredients (API) and then focus on methodologies for the continuous processing of slurries and solids, which present key challenges in enabling the overall continuous manufacturing process. Crystallization is the key unit operation that connects the primary (API synthesis) and secondary (design of delivery form) manufacturing processes. The solid properties such as shape and crystal size distribution (CSD) of the API obtained at the crystallization step will strongly influence the efficiency of the secondary manufacturing process. Modeling and control approaches for continuous crystallization will be presented that allow better control of the product CSD.
Continuous secondary manufacturing of the final product from the API isolated at the crystallization step has also received quite a bit of attention in the industry and been the focus of research in the NSF Engineering Research Center for Structured Organic Particulate Systems, a multi-university collaboration with industry. The focus here again is on innovative use of on-line measurement, process modelling and control, exceptional events management and real time process management. Process configurations including the full range of unit operations from powder feeding to tablet coating are under active consideration. The rudiments of process flowsheet modelling for such operations are beginning to be assembled offering the potential for design optimization. The presentation will conclude with a brief overview of some additional manufacturing innovations targeting small scale manufacturing configurations suitable for delivery of individualized medicine.
Dr Nagy is a Professor of Chemical Engineering at Purdue University and also holds a European Research Council Research Adjunct Professorship at Loughborough University, UK, where he was a professor of process systems engineering and Director of the Departmental Pharmaceutical Engineering Research Centre, before joining Purdue in Fall 2012.
Dr Nagy has over 12 years of experience in advanced process control, process analytical technologies and crystallization modeling and control approaches. His current research focuses on the application of systems approaches and tools in the design and robust control of batch and continuous crystallization systems and integrated particulate manufacturing processes for pharmaceutical applications. He has more than 200 publications in these areas, and has given numerous invited talks at conferences, universities and companies worldwide.
Dr Nagy is the Founding Editor of the Pharmaceutical Engineering Subject area of Chemical Engineering Research and Design, and associate editor for several other three journals in the area of process control. Dr Nagy is member of the stirring committee of the American Association for Crystallization Technologies, and the Crystallization Working Party of the European Federation of Chemical Engineers. He received major awards and best paper prizes from IEEE, IFAC, European Federation of Chemical Engineering, Institute of Chemical Engineering, Council of Chemical Research, Royal Academy of Engineering and the European Research Council.
G.V. Rex Reklaitis is Burton and Kathryn Gedge Distinguished Professor of Chemical Engineering at Purdue University and currently deputy director of the ERC on Structured Organic Particulate Systems. At Purdue he has served as the Head of the School of Chemical Engineering and Director of the Computer Integrated Process Operations Center. His expertise lies in process systems engineering, the application of information and computing technologies to process and product design, process operations and supply chain management. Current research interests include applications of process systems methodology to improve pharmaceutical product design, development, manufacture and administration as well as systems studies of integrated energy networks.
He was educated at the Illinois Institute of Technology (BS ChE), received MS and PhD degrees from Stanford University, has held an NSF Postdoctoral fellowship (Zurich, Switzerland) and Senior Fulbright Lectureship (Vilnius, Lithuania). He is a member of the US National Academy of Engineering, fellow of AIChE, and past Editor-in-Chief of Computers & Chemical Engineering. He has received the Computing in Chemical Engineering Award (AICHE), the ChE Lectureship Award (ASEE), the George Lappin and Van Antwerpen Awards (AIChE) and the Long Term Achievements in Computer Aided Process Engineering Award of the European Federation of Chemical Engineering and the Illinois Institute of Technology Professional Achievement Award. He has served on the Board of Directors of AICHE, the Council for Chemical Research and the CACHE Corporation. He has published over 240 papers and book chapters and edited/authored eight books.
Heavy dependence on petroleum and high greenhouse gas (GHG) emissions from the production, distribution, and consumption of hydrocarbon fuels pose serious challenges for the United States (US) transportation sector. Depletion of domestic petroleum sources combined with a volatile global oil market prompt the need to discover alternative fuel-producing technologies that utilize domestically abundant sources. The primary aim in the discovery of hybrid energy processes is to combine coal, biomass, and natural gas to meet the United States transportation fuel demand.
The first part of this presentation will outline the needs and introduce novel hybrid feedstock coal, biomass, and natural gas to liquids (CBGTL) process alternatives. The second part will address important decisions at the process design and process synthesis level. A thermochemical based process superstructure, its mixed-integer nonlinear optimization (MINLP) model, and systematic approaches for its global optimization will be discussed. The third part will introduce a novel framework for the optimal energy supply chain of CBGTL processes. The optimal network topology provides information on (i) the optimal plant locations throughout the country, (ii) the locations of feedstock sources, (iii) the interconnectivity between the feedstock source locations, CBGTL plants locations, and the demand locations, (iv) the modes of transportation used in each connection, and (v) the flow rate amounts of each feedstock and product type. Life cycle analysis on the nationwide energy supply chain shows that at least 50% reduction of GHG emissions is attainable.
Dr. Floudas is the Stephen C. Macaleer ’63 Professor in Engineering and Applied Science, Professor of Chemical and Biological Engineering at Princeton University, Faculty in the Center for Quantitative Biology at Princeton University’s Lewis-Sigler Institute, Associated Faculty in the Program of Computational and Applied Mathematics at Princeton University, Department of Operations Research and Financial Engineering at Princeton University, and the Andlinger Center for Energy and the Environment. He earned his B.S.E. in 1982 at Aristotle University of Thessaloniki, Greece, completed his Ph.D. in December 1985 at Carnegie Mellon University. Professor Floudas is the author of two graduate textbooks, Nonlinear Mixed-Integer Optimization (Oxford University Press, 1995), and Deterministic Global Optimization (Kluwer Academic Publishers, 2000). He has co-edited ten monographs/books, has over 270 refereed publications, and is the chief co-editor of the Encyclopedia of Optimization (Kluwer Academic Publishers, 2001; 2nd edition, Springer, 2008). He is the recipient of numerous awards and honors for teaching and research that include the NSF Presidential Young Investigator Award, 1988; the Engineering Council Teaching Award, Princeton University, 1995; the Bodossaki Foundation Award in Applied Sciences, 1997; the Best Paper Award in Computers and Chemical Engineering, 1998; the Aspen Tech Excellence in Teaching Award, 1999; the 2001 AIChE Professional Progress Award for Outstanding Progress in Chemical Engineering; the 2006 AIChE Computing in Chemical Engineering Award; the 2007 Graduate Mentoring Award, Princeton University; and Member of National Academy of Engineering, 2011.
In a fossil-fuel deprived world, it is likely that all the basic human needs will be met by renewable sources like solar energy. Among the needs, transportation offers the greatest challenges, owing to its high energy-density fuel requirements, which have traditionally been met by liquid hydrocarbon fuels derived from fossil resources. Here, we present a detailed systems analysis of the transportation sector, from which emerges an energy efficient roadmap, based on the use of renewable carbon sources like biomass, solar energy in the form of H2, heat and electricity, in conjunction with novel processes for producing liquid fuels. In addition, some specific transition solutions are also discussed.
In a sustainable energy future, availability of efficient hydrogen from solar energy will be a key to the large scale production of chemicals and fuels. We present process synthesis methodology to identify efficient processes for solar hydrogen production. These processes, although not economical today, point us in the direction where technical advancements are needed to enable a truly sustainable future.
Finally, a grand challenge of solar energy use is its intermittency. Synthesis of processes to store GWhr levels of energy for uninterrupted power grid supply is also discussed.
Rakesh Agrawal is Winthrop E. Stone Distinguished Professor, School of Chemical Engineering, Purdue University. Previously, he was an Air Products Fellow at Air Products and Chemicals, Inc., where he worked until 2004.
A major thrust of his research is related to energy issues and includes novel processes for fabrication of low-cost solar cells, biomass and liquid fuel conversion, and energy systems analysis. His research further includes synthesis of muticomponent separation configurations including distillation, membrane and adsorption based processes, basic and applied research in gas separations, process development, gas liquefaction processes and cryogenics. He was a member of the NRC Board on Energy and Environmental Systems (BEES) and a member of the AIChE’s Board of Directors and also its Energy Commission. He has published 116 technical papers and holds 118 U.S. and more than 500 foreign patents. These patents are used in over one hundred chemical plants with total capital expenditure in multibillion dollars.
He is a member of the US National Academy of Engineering, a Fellow of the American Academy of Arts and Sciences and a foreign Fellow of the Indian National Academy of Engineering. He is currently on the Technical Advisory boards of five chemical companies. Agrawal received the 2010 National Medal of Technology and Innovation from the U.S. President.
Dr. Agrawal received a B. Tech. from the Indian Institute of Technology, in Kanpur, India; a M.Ch.E. from the University of Delaware, and an Sc.D. in chemical engineering from the MIT.