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Based on the author's forty years of teaching experience, this unique textbook covers both basic and advanced concepts of optimization theory and methods for process systems engineers. Topics covered include continuous, discrete and logic optimization (linear, nonlinear, mixed-integer and generalized disjunctive programming), optimization under uncertainty (stochastic programming and flexibility analysis), and decomposition techniques (Lagrangean and Benders decomposition). Assuming only a basic background in calculus and linear algebra, it enables easy understanding of mathematical reasoning, and numerous examples throughout illustrate key concepts and algorithms. End-of-chapter exercises involving theoretical derivations and small numerical problems, as well as in modeling systems like GAMS, enhance understanding and help put knowledge into practice. Accompanied by two appendices containing web links to modeling systems and models related to applications in PSE, this is an essential text for single-semester, graduate courses in process systems engineering in departments of chemical engineering.

The design of efficient supply chains is a major challenge for companies in the process industry. Supply chain performance is subject to different sources of uncertainty including reliability of the facilities. Facility disruptions are among the most critical events that supply chains can experience. In order to reduce the undesirable effects of disruptions, these events must be anticipated at the design phase of the supply chain. This work addresses the design of supply chains under the risk of facility disruptions by simultaneously considering decisions on the facility location and the inventory management. The proposed formulation is based on a two-stage stochastic programming framework where the scenarios are determined by the possible combinations of facility disruptions. The first stage decisions include the location of distribution centers and their storage capacity. The second stage decisions involve assigning customer demands to the distribution centers that are available in every scenario. The objective is to minimize the sum of investment cost and the expected cost of distribution during a finite time horizon. The formulation is implemented and compared with the optimal solution of the deterministic design problem though an illustrative example. The results show that the proposed formulation generates supply chain designs with the capability to adjust to adverse scenarios. This flexibility translates into significant savings when disruptions occur in the operation of supply chains.

The Workshop on Simulation and Optimization for Sustainable Engineering is held in Santander (September 28th-29th) on the occasion of the visit of Prof. Ignacio Grossmann to the Department of Chemical and Biomolecular Engineering of the Universidad de Cantabria in the framework of the Fulbright U.S. Specialist Program. This workshop is organized in collaboration with AQUIQÁN, the Association of Chemistry and Chemical Engineering of Cantabria, with the aims of serving both as a forum of discussion of the recent advances in the topic and a meeting point of the closest collaborators of Prof. Grossmann in Spain over the last years. Overall, 30 researchers will take part in this event coming from several universities (Alicante, Cantabria, Rovira i Virgili, Salamanca, Sevilla, and Valladolid) and the IMDEA Materials Institute. The program includes a plenary lecture imparted by Prof. Grossmann, one keynote presentation representative of each institution and around 15 oral presentations from young researchers.

A unique text covering basic and advanced concepts of optimization theory and methods for process systems engineers. With examples illustrating key concepts and algorithms, and exercises involving theoretical derivations, numerical problems and modeling systems, it is ideal for single-semester, graduate courses in process systems engineering.

In this work a nonlinear mathematical programming model seeking the optimal balance of feedstocks to manufacture multiple polymer grades in a polypropylene production facility is described. To obtain the best possible return on assets, this type of continuous plant usually operates at maximum capacity while attempting to reduce the production costs. The main units of the process are a distillation column and a polymerization reactor, for which approximate but practically accurate process models were developed. Both a single and multiple product formulations are presented. The latter determines the optimal operating conditions for a given schedule of products, which includes several demands on different product families. By selecting the flowrates of the available feedstocks (refinery and chemical grade) with the production rates of each product, the proposed method seeks the minimum production cost that maximizes the plant throughput. The tradeoff between feedstocks costs and production rates is analyzed by solving the model with different time horizons, and also an additional “slack” product is considered to investigate the implication of having some extra production when the schedule finishes earlier. The proposed formulation with a user-friendly interface is being deployed to assist with purchase decisions.

This contribution presents a multi-period synthesis of an optimally-integrated regional biorefinery's supply networks, based on a Mixed-Integer Linear Programming (MILP) model. The production processes from different sources of biomass include first, second, and third generations of biofuels such as bioethanol, biodiesel, hydrogen, Fischer-Tropsch (FT)-diesel, and green gasoline. The aim is to maximize the economically optimal utilization of seasonal and year-round continuously harvested raw materials from regionally-located available biomass resources, by considering the competition between fuels and food production. The proposed multi-period MILP model enables efficient bioenergy network synthesis and optimization. Economically optimal solutions are obtained, with optimal selection of technologies, raw materials, intermediate and final products, and the timely-optimal planning of harvesting, biofuels production, storage, and logistics.

Over the last 20 years, fundamental design concepts and advanced computer modeling have revolutionized process design for chemical engineering. Team work and creative problem solving are still the building blocks of successful design, but new design concepts and novel mathematical programming models based on computer-based tools have taken out much of the guess-work. This book presents the new revolutionary knowledge, taking a systematic approach to design at all levels.

In this work a nonlinear mathematical programming model seeking the optimal balance of feedstocks to manufacture multiple polymer grades in a polypropylene production facility is described. To obtain the best possible return on assets, this type of continuous plant usually operates at maximum capacity while attempting to reduce the production costs. The main units of the process are a distillation column and a polymerization reactor, for which approximate but practically accurate process models were developed. Both a single and multiple product formulations are presented. The latter determines the optimal operating conditions for a given schedule of products, which includes several demands on different product families. By selecting the flowrates of the available feedstocks (refinery and chemical grade) with the production rates of each product, the proposed method seeks the minimum production cost that maximizes the plant throughput. The tradeoff between feedstocks costs and production rates is analyzed by solving the model with different time horizons, and also an additional “slack” product is considered to investigate the implication of having some extra production when the schedule finishes earlier. The proposed formulation with a user-friendly interface is being deployed to assist with purchase decisions.

This contribution presents a multi-period synthesis of an optimally-integrated regional biorefinery's supply networks, based on a Mixed-Integer Linear Programming (MILP) model. The production processes from different sources of biomass include first, second, and third generations of biofuels such as bioethanol, biodiesel, hydrogen, Fischer-Tropsch (FT)-diesel, and green gasoline. The aim is to maximize the economically optimal utilization of seasonal and year-round continuously harvested raw materials from regionally-located available biomass resources, by considering the competition between fuels and food production. The proposed multi-period MILP model enables efficient bioenergy network synthesis and optimization. Economically optimal solutions are obtained, with optimal selection of technologies, raw materials, intermediate and final products, and the timely-optimal planning of harvesting, biofuels production, storage, and logistics.