Выход загруженных веществ in vitro из системы упорядоченных биодеградируемых отдельностоящих микрокамер

We address in this paper the optimization of a multi-site, multi-period, and multi-product planning problem with sequence-dependent changeovers, which is modeled as a mixed-integer linear programming (MILP) problem. Industrial instances of this problem require the planning of a number of production and distribution sites over a time span of several months. Temporal and spatial Lagrangean decomposition schemes can be useful for solving these types of large-scale production planning problems. In this paper we present a theoretical result on the relative size of the duality gap of the two decomposition alternatives. We also propose a methodology for exploiting the economic interpretation of the Lagrange multipliers to speed the convergence of numerical algorithms for solving the temporal and spatial Lagrangean duals. The proposed methods are applied to the multi-site multi-period planning problem in order to illustrate their computational effectiveness.</p

Advances in mathematical programming models for enterprise-wide optimization

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 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. Finally, based on the EWO program at the Center of Advanced Process Decision-making at Carnegie Mellon, we describe several applications to show the potential of this area.</p

Mixed-integer programming : is it a useful tool in process synthesis?

Technical Repor

Offshore Oilfield Development Planning under Uncertainty and Fiscal Considerations

The objective of this paper is to present a unified modeling framework to address the issues of uncertainty and complex fiscal rules in the development planning of offshore oil and gas fields which involve critical investment and operational decisions. In particular, the paper emphasizes the need to have as a basis an efficient deterministic model that can account for various alternatives in the decision making process for a multi-field site incorporating sufficient level of details in the model, while being computationally tractable for the large instances. Consequently, such a model can effectively be extended to include other complexities, for instance endogenous uncertainties and a production sharing agreements. Therefore, we present a new deterministic MINLP model followed by discussion on its extensions to incorporate generic fiscal rules, and uncertainties based on recent work on multistage stochastic programming. Numerical results on the development planning problem for deterministic as well as stochastic instances are discussed. A detailed literature review on the modeling and solution methods that are proposed for each class of the problems in this context is also presented.</p

Stochastic Inventory Management for Tactical Process Planning under Uncertainties: MINLP Models and Algorithms

We address in this paper the mid-term planning of chemical complexes with integration of stochastic inventory management under supply and demand uncertainty. By using the guaranteed service approach to model the time delays in the chemical flows inside the chemical process network, we capture the stochastic nature of the supply and demand variations, and develop an equivalent deterministic optimization model to minimize the total cost including production cost, feedstock purchase cost, cycle inventory and safety stock costs. The model simultaneously determines the optimal purchases of the feedstocks, production levels of the processes, sales of final products and safety stock levels of all the chemicals, as well as the internal demand of the production processes. The model also captures “risk-pooling” effects to allow centralization of inventory management for chemicals that are consumed/produced by multiple processes. We formulate the model as a mixed-integer nonlinear program (MINLP) with a nonconvex objective function and nonconvex constraints. To solve the global optimization problem with modest computational times, we exploit some model properties and develop a tailored branch-and-refine algorithm based on successive piece-wise linear approximation. Five examples are presented to illustrate the application of the models and the performance of the proposed algorithm.</p

Multistage stochastic programming approach for offshore oilfield infrastructure planning under production sharing agreements and endogenous uncertainties

<p>The paper presents a new optimization model and solution approach for the investment and operations planning of offshore oil and gas field infrastructure. As compared to the conventional models where either fiscal rules or uncertainty in the field parameters is considered, the proposed model is the first one in the literature that includes both of these complexities in an efficient manner. In particular, a tighter formulation for the production sharing agreements based on our recent work, and a perfect positive or negative correlation among the endogenous uncertain parameters (field size, oil deliverability, water–oil ratio and gas–oil ratio) is considered to reduce the total number of scenarios in the resulting multistage stochastic formulation. To solve the large instances of the problem, a Lagrangean decomposition approach allowing parallel solution of the scenario subproblems is implemented in the GAMS grid computing environment. Computational results on a variety of oilfield development planning examples are presented to illustrate the efficiency of the model and the proposed solution approach.</p

Multicut Benders Decomposition Algorithm for Process Supply Chain Planning under Uncertainty

In this paper, we present a multicut version of the Benders decomposition method for solving two-stage stochastic linear programming problems, including stochastic mixed-integer programs with only continuous recourse (two-stage) variables. The main idea is to add one cut per realization of uncertainty to the master problem in each iteration, that is, as many Benders cuts as the number of scenarios added to the master problem in each iteration. Two examples are presented to illustrate the application of the proposed algorithm. One involves production-transportation planning under demand uncertainty, and the other one involves multiperiod planning of global, multiproduct chemical supply chains under demand and freight rate uncertainty. Computational studies show that while both the standard and the multicut versions of the Benders decomposition method can solve large-scale stochastic programming problems with reasonable computational effort, significant savings in CPU time can be achieved by using the proposed multicut algorithm.</p

Energy and Water Optimization in Biofuel Plants

In this paper we address the topic of energy and water optimization in the production of bioethanol from corn and switchgrass. We show that in order for these manufacturing processes to be attractive, there is a need to go beyond traditional heat integration and water recycling techniques. Thus, we propose a strategy based on mathematical programming techniques to model and optimize the structure of the processes, and perform heat integration including the use of multi-effect distillation columns and integrated water networks to show that the energy efficiency and water consumption in bioethanol plants can be significantly improved. Specifically, under some circumstances energy can even be produced and the water consumption can be reduced below the values required for the production of gasoline.</p

Optimal engineered algae composition for the integrated simultaneous production of bioethanol and biodiesel

<p>The optimization of the composition of the algae for the simultaneous production of bioethanol and biodiesel is presented. We consider two alternative technologies for the biodiesel synthesis from algae oil, enzymatic or homogeneous alkali catalyzed that are coupled with bioethanol production from algae starch. In order to determine the optimal operating conditions, we not only couple the technologies, but simultaneously optimize the production of both biofuels and heat integrate them while optimizing the water consumption. Multi-effect distillation is included to reduce the energy and cooling water consumption for ethanol dehydration. In both cases, the optimal algae composition results in 60% oil, 30% starch, and 10% protein. The best alternative for the production of biofuels corresponds to a production price of 0.35 $/gal, using enzymes, with energy and water consumption values (4.00 MJ/gal and 0.59 gal/gal).</p

Multilevel-hierarchical MNLP [i.e., MINLP] synthesis of process flowsheets

Abstract: "The objective of this contribution is to propose a multilevel-hierarchical approach to the MINLP synthesis of process flowsheets. Following a hierarchical strategy, the designer can postulate the superstructure at different levels of representation of flowsheet alternatives and model it at the corresponding level of aggregation and complexity. By the use of the prescreening procedure the superstructure is optimized more effectively and reliably. The approach enables one to address different process operations like reactions, connectivity and species allocation, separation, energy and heat integration and HEN through simultaneous superstructure optimization.