Self-Optimizing Steady-State Back-Off Approach for Control Structure Selection

The selection of suitable control structures has an important influence on the economic performance of process systems in the presence of disturbances. Economics has been incorporated in the control structure selection problem using different formulations based on different criteria. The back-off approach is based on the idea of minimizing the economic loss that results from the need to back off from the active constraints to avoid violating them in the presence of disturbances. On the other hand, self-optimizing control schemes aim at selecting controlled variables and constant setpoint values, such that the economic loss with respect to optimal operation is minimized in the presence of disturbances. This paper presents a comprehensive study of different formulations of the back-off approach that pays attention to steady-state feasibility in the presence of disturbances. We argue that the back-off approach that selects controlled variables and optimal setpoint values by minimizing the average cost in the presence of disturbances is a global self-optimizing control approach. The performance of the different formulations is compared by means of three different case studies.Fil: Bottari, Agustin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; ArgentinaFil: Marchetti, Pablo Andres. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaFil: Marchetti, Alejandro Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; Argentin

First World Consensus Conference on pancreas transplantation: Part II - recommendations.

Funder: Fondazione Pisa, Pisa, Italy; Id: http://dx.doi.org/10.13039/100007368Funder: Tuscany Region, Italy; Id: http://dx.doi.org/10.13039/501100009888Funder: Pisa University Hospital, Pisa, ItalyFunder: University of Pisa, Pisa, Italy; Id: http://dx.doi.org/10.13039/501100007514The First World Consensus Conference on Pancreas Transplantation provided 49 jury deliberations regarding the impact of pancreas transplantation on the treatment of diabetic patients, and 110 experts' recommendations for the practice of pancreas transplantation. The main message from this consensus conference is that both simultaneous pancreas-kidney transplantation (SPK) and pancreas transplantation alone can improve long-term patient survival, and all types of pancreas transplantation dramatically improve the quality of life of recipients. Pancreas transplantation may also improve the course of chronic complications of diabetes, depending on their severity. Therefore, the advantages of pancreas transplantation appear to clearly surpass potential disadvantages. Pancreas after kidney transplantation increases the risk of mortality only in the early period after transplantation, but is associated with improved life expectancy thereafter. Additionally, preemptive SPK, when compared to SPK performed in patients undergoing dialysis, appears to be associated with improved outcomes. Time on dialysis has negative prognostic implications in SPK recipients. Increased long-term survival, improvement in the course of diabetic complications, and amelioration of quality of life justify preferential allocation of kidney grafts to SPK recipients. Audience discussions and live voting are available online at the following URL address: http://mediaeventi.unipi.it/category/1st-world-consensus-conference-of-pancreas-transplantation/246

An approximate mathematical framework for resource-constrained multistage batch scheduling

A rigorous representation of the multistage batch scheduling problem is often useless to even provide a good feasible schedule for many real-world industrial facilities. In order to derive a much simpler scheduling methodology, some usual features of multistage batch plants should be exploited. A common observation in industry is that multistage processing structures usually present a bottleneck stage (BS) controlling the plant output level. Therefore, the quality of the production schedule heavily depends on the proper allocation and sequencing of the tasks performed at the stage BS. Every other part of the processing sequence should be properly aligned with the selected timetable for the bottleneck tasks. A closely related concept with an empirical basis is the usual existence of a common batch sequencing pattern along the entire processing structure that leads to define the constant-batch-ordering rule (CBOR). According to this rule, a single sequencing variable is sufficient to establish the relative ordering of two batches at every processing stage in which both have been allocated to the same resource item. This work introduces a CBOR-based global precedence formulation for the scheduling of order-driven multistage batch facilities. The proposed MILP approximate problem representation is able to handle sequence-dependent changeovers, delivery due dates and limited manufacturing resources other than equipment units. Optimal or near-optimal solutions to several large-scale examples were found at very competitive CPU times.Fil: Marchetti, Pablo Andres. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaFil: Cerda, Jaime. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentin

A general resource-constrained scheduling framework for multistage batch facilities with sequence-dependent changeovers

This work introduces a new MILP sequential approach to the short-term scheduling of multistage batch plants that accounts for sequence-dependent changeover times, intermediate due dates and limited availability of renewable resources. It relies on a continuous-time formulation based on the general precedence notion that uses different sets of binary variables to handle allocation and sequencing decisions. To avoid resource overloading, additional constraints in terms of sequencing variables and a new set of 0-1 overlapping variables are presented. They allow tracking the set of tasks requiring the same resource and running in parallel at the start of another process operation. In this way, the proposed formulation involves a reasonable number of binary variables and constraints and features a very good computational behavior, even in the presence of hard bottleneck resources. Four illustrative examples, one of them including multiple bottleneck resources shared by several processing stages, have been efficiently solved.Fil: Marchetti, Pablo Andres. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaFil: Cerda, Jaime. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentin

A continuous-time tightened formulation for single-stage batch scheduling with sequence dependent changeovers

This work presents a new mixed-integer linear programming (MILP) continuous-time approach for the shortterm scheduling of single-stage multiproduct batch plants with parallel units and sequence-dependent changeovers. It uses a unit-specific precedence-based representation, combined with effective, nontrivial tightening constraints, to develop a very efficient problem formulation. The additional cuts account for the updated information provided by allocation and sequencing binary variables to systematically reduce the solution space of the corresponding LP at every node of the enumeration tree. In this way, close bounds for key variables like makespan, task earliness, and task starting/completion times are generated and continually improved throughout the search in order to accelerate the node pruning process. Alternative problem objectives like the minimum total earliness or the shortest makespan can be managed. To make a thorough comparison with previous continuous-time scheduling approaches, several benchmark examples have been solved. Results show that the proposed approach usually presents the best computational performance.Fil: Marchetti, Pablo Andres. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaFil: Cerda, Jaime. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentin

Simultaneous lot sizing and scheduling of multistage batch processes handling multiple orders per product

A pair of precedence-based continuous-time formulations addressing the combined lot sizing and scheduling of order-driven multistage batch facilities is presented. The proposed mixed-integer linear programming (MILP) models can handle multiple orders per product with different delivery dates, variable processing times, and sequence-dependent changeovers. As each order may be filled by one or more batches, enough batches for each order ensuring optimality are initially defined. The two monolithic formulations are intended for sequential batch processes where batch integrity is preserved throughout the entire production system. However, lots of final products can be split to satisfy two or more orders. One of the approaches is based on a detailed MILP formulation allocating individual batches to units and ordering them in every unit. In contrast, the second methodology is specially designed for large scheduling problems. It first gathers batches for the same order into clusters, and then assigns clusters to units and sequences groups of batches in every unit. The larger the number of groups, the more rigorous is the cluster-based formulation. Alternative sequencing constraints based on reliable assumptions were also tested. Several examples involving up to 92 batches have been successfully solved using one or both formulations.Fil: Marchetti, Pablo Andres. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); ArgentinaFil: Mendez, Carlos Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); ArgentinaFil: Cerda, Jaime. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); Argentin

Mixed-integer linear programming monolithic formulations for lot-sizing and scheduling of single-stage batch facilities

This paper presents a pair of mixed-integer linear programming (MILP) continuous-time formulations for the simultaneous lot-sizing and scheduling of single-stage multiproduct batch facilities. Both approaches can handle multiple customer orders per product at different due dates as well as variable processing times. To match product demands, several batches can be allocated to a single requirement and, at the same time, a single batch may be used to satisfy multiple orders. Through a novel procedure, a predefined set of batches for each order with enough elements to guarantee optimality is generated. The two proposed formulations deal with batch sequencing decisions in a different manner. One of them rigorously arranges individual batches assigned to the same unit, while the other sequences clusters of batches sharing the same product and due date, and processed in the same equipment item. Grouping batches into clusters seeks to reduce the number of product changeovers. The final contents of clusters are model decisions. Powerful symmetry breaking constraints based on allocation variables to avoid redundant solutions were also developed. Three cases studies involving up to 56 batches have been solved. The two formulations provide very good results at quite competitive CPU times when compared with prior monolithic techniques. Moreover, the approximate cluster-based method was able to solve very large problems in an efficient manner. It was validated by comparing its results with the ones provided by the rigorous model.Fil: Marchetti, Pablo Andres. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); ArgentinaFil: Mendez, Carlos Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); ArgentinaFil: Cerda, Jaime. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); Argentin

Multivariable control structure design based on mixed-integer quadratic programming

In this work a new approach to address multivariable control structure (MCS) design for medium/large-scale processes is proposed. The classical MCS design methodologies rely on superstructure representations which define sequential and/or bilevel mixed-integer nonlinear programming (MINLP) problems. The main drawbacks of this kind of approach are the complexity of the required solution methods (stochastic/deterministic global search), the computational time, and the optimality of the solution when simplifications are made. Instead, this work shows that, by using the sum of squared deviations (SSD) as well as the net load evaluation (NLE) concepts, the control structure design problem can be formulated as a mixed-integer quadratic programming (MIQP) model with linear constraints, featuring both optimality and improved computational performance due to state-of-the-art solvers. The formulation is implemented in the GAMS environment using CPLEX as the selected solver and two typical case studies are presented to show the benefits of the proposed approach.Fil: Braccia, Lautaro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; ArgentinaFil: Marchetti, Pablo Andres. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaFil: Luppi, Patricio Alfredo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; ArgentinaFil: Zumoffen, David Alejandro Ramon. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; Argentina. Universidad Tecnológica Nacional. Facultad Regional Rosario; Argentin

Rolling Horizon Approach for Production-Distribution Coordination of Industrial Gases Supply Chains

This paper addresses industrial gases supply chains involving multiple products at multiple plants that must be coordinated with multiple depot-truck-routes in order to satisfy customer demands. The full-space optimization problem corresponds to a large-scale mixed-integer linear programming model (MILP). To solve large-scale industrial problems, this paper proposes a rolling horizon approach with two aggregation strategies for solving the smaller subproblems. The first one relies on the linear programming (LP) relaxation for which the binary variables (complicating variables) of the distribution problem are treated as continuous, while the second one uses a novel tailored model for the distribution side constraints that leads to improved solutions. A real case study of an industrial gases supply chain has been addressed obtaining good results in both objective value and with lower computational effort compared with the full-space solution. The extension to longer time horizons through a receding horizon is also considered.Fil: Zamarripa, Miguel. University Of Carnegie Mellon. Department Of Chemical Engineering; Estados UnidosFil: Marchetti, Pablo Andres. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico Para la Industria Química; ArgentinaFil: Grossmann, Ignacio. University Of Carnegie Mellon. Department Of Chemical Engineering; Estados UnidosFil: Singh, Tejinder. Delaware Research and Technology Center; Estados UnidosFil: Lotero, Irene. Delaware Research and Technology Center; Estados UnidosFil: Gopalakrishnan, Ajit. Delaware Research and Technology Center; Estados UnidosFil: Besancon, Brian. Delaware Research and Technology Center; Estados UnidosFil: André, Jean. Claude & Delorme R&D Center; Franci

Simultaneous Production and Distribution of Industrial Gas Supply-Chains

In this paper, we propose a multi-period mixed-integer linear programming model for optimal enterprise-level planning of industrial gas operations. The objective is to minimize the total cost of production and distribution of liquid products by coordinating production decisions at multiple plants and distribution decisions at multiple depots. Production decisions include production modes and rates that determine power consumption. Distribution decisions involve source, destination, quantity, route, and time of each truck delivery. The selection of routes is a critical factor of the distribution cost. The main goal of this contribution is to assess the benefits of optimal coordination of production and distribution. The proposed methodology has been tested on small, medium, and large size examples. The results show that significant benefits can be obtained with higher coordination among plants/depots in order to fulfill a common set of shared customer demands. The application to real industrial size test cases is also discussed.Fil: Marchetti, Pablo Andres. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química (i); Argentina. University Of Carnegie Mellon; Estados UnidosFil: Gupta, Vijay. University Of Carnegie Mellon. Department Of Chemical Engineering; Estados UnidosFil: Grossmann, Ignacio E.. University Of Carnegie Mellon. Department Of Chemical Engineering; Estados UnidosFil: Cook, Lauren. Delaware Research and Technology Center; Estados UnidosFil: Valton , Pierre Marie. Air Liquide - Paris Saclay R&d Center; FranciaFil: Singh, Tejinder. Delaware Research and Technology Center; Estados UnidosFil: Li, Tong. Delaware Research and Technology Center; Estados UnidosFil: André, Jean. Air Liquide - Paris Saclay R&d Center; Franci