Gabriela Corsano

Optimal design for sustainable bioethanol supply chain considering detailed plant performance model

Abstract The always increasing energy demand combined with the declining availability of fossil fuels is driving forces for the investigation of renewable energy sources. In this context, bioethanol is considered as one of the most appropriate solutions for short term gasoline substitution. Then, the motivation of this work is to propose a MINLP optimization model for a sustainable design and behavior analysis of sugar/ethanol supply chain (SC). A detailed model for ethanol plant design is embedded in the SC model, and therefore plant and SC designs are simultaneously obtained. Yeast production and residue recycles are taken into account in order to assess the environmental impact. The inclusion of sustainability issues in the model produces both economic and operative changes in SC and plant designs. The simultaneous optimization of these elements allows the evaluation of several compromises among design and process variables. These issues are highlighted throughout the evaluated studied cases.

Mathematical modeling for simultaneous design of plants and supply chain in the batch process industry

Most supply chain design models have focused on the integration problem, where links among nodes must be settled in order to allow an efficient operation of the whole system. At this level, all the problem elements are modeled like black boxes, and the optimal solution determines the nodes allocation and their capacity, and links among nodes. In this work, a new approach is proposed where decisions about plant design are simultaneously made with operational and planning decisions on the supply chain. Thus, tradeoffs between the plant structure and the network design are assessed. The model considers unit duplications and the allocation of storage tanks for plant design. Using different sets of discrete sizes for batch units and tanks, a mixed integer linear programming model (MILP) is attained. The proposed formulation is compared with other non-integrated approaches in order to illustrate the advantages of the presented simultaneous approach.

Simultaneous design and scheduling of a semicontinuous/batch plant for ethanol and derivatives production

Abstract The interest on renewable fuels has greatly increased in the last years. Particularly, ethanol production arises as a good solution to many current economic-environmental problems. Yeast production from the ethanol residuals constitutes a sustainable alternative. Usually, this kind of plants is designed using single product campaigns. However, since yeast degradation is fast and a continuous supply must be assured, the mixed product campaign policy is the most appropriate. Besides, a stable context can be assumed to justify this approach that takes advantage of the special structure of the plant. Therefore, in this paper, a mixed integer linear programming model is formulated for simultaneous design and scheduling of a semicontinuous/batch plant for ethanol and derivatives production. The optimal plant configuration, unit sizes, number of batches of each product in the campaign and its sequencing is obtained in order to fulfill the ethanol and yeast demands minimizing the investment cost.

Computational methods for the simultaneous strategic planning of supply chains and batch chemical manufacturing sites

Abstract In this work we present efficient solution strategies for the task of designing supply chains with the explicit consideration of the detailed plant performance of the embedded facilities. Taking as a basis a mixed-integer linear programming (MILP) model introduced in a previous work, we propose three solution strategies that exploit the underlying mathematical structure: A bi-level algorithm, a Lagrangean decomposition method, and a hybrid approach that combines features from both of these two methods. Numerical results show that the bi-level method outperforms the others, leading to significant CPU savings when compared to the full space MILP.

Using pareto filters to support risk management in optimization under uncertainty: Application to the strategic planning of chemical supply chains

Abstract Optimization under uncertainty has attracted recently an increasing interest in the process systems engineering literature. The inclusion of uncertainties in an optimization problem inevitably leads to the need to manage the associated risk in order to control the variability of the objective function in the uncertain parameters space. So far, risk management methods have focused on optimizing a single risk metric along with the expected performance. In this work we propose an alternative approach that can handle several risk metrics simultaneously. First, a multi-objective stochastic model containing a set of risk metrics is formulated. This model is then solved efficiently using a tailored decomposition strategy inspired on the Sample Average Approximation. After a normalization step, the resulting solutions are assessed using Pareto filters, which identify solutions showing better performance in the uncertain parameters space. The capabilities and benefits of our approach are illustrated through a design and planning supply chain case study.

Generalized disjunctive programming model for the multi-period production planning optimization: An application in a polyurethane foam manufacturing plant

Abstract A Generalized Disjunctive Programming (GDP) model for the optimal multi-period production planning and stock management is proposed in this work. The formulation is applied to a polyurethane foam manufacturing plant that comprises three stages: a first step that produces pieces with certain characteristics, a second process that involves the location of these pieces in a limited area and a third stage where pieces are stored in dedicated spaces. This article shows the GDP capabilities to provide a qualitative framework for representing the problem issues and their connections in a natural way, especially in a context where decisions integration is required. Due to the multi-period nature of the planning problem, a rolling horizon approach is suitable for solving it in reasonable computing time. It serves as a tool for analyzing the trade-offs among the different costs. Through the examples, the capabilities of the formulation and the proposed resolution method are highlighted.

Integration of Organosolv Process for Biomass Pretreatment in a Biorefinery

Abstract Biorefinery development by the bioconversion of lignocellulosic biomass has attracted much interest in recent years. The pretreatment process plays an important role in the commercialized production of several products, including cellulosic ethanol. From the point of view of the integrated utilization of lignocellulosic biomass, organosolv pretreatment provides a pathway for the biorefining of biomass. This chapter gives an introduction about the organosolv process and presents/discuss the integration of this technique for sugarcane bagasse pretreatment in a diversified sugar industry. Economic evaluation of different alternatives to biorefinery were performed. Finally, the synthesis and optimal design of each batch step of the proposal alternatives are included. The optimal configuration of stages, the number of units in each stage, the unit sizes, and the minimum total production costs are obtained from the global optimization model and superstructure proposed.

OPTIMAL DESIGN FOR AN ETHANOL PLANT COMBINING FIRST AND SECOND-GENERATION TECHNOLOGIES

The synthesis and optimal design of batch plants is addressed in this study. It was applied to the technology of conventional ethanol production in a Cuban distillery using the product of enzymatic hydrolysis of pretreated bagasse as another sugared substrate, starting from laboratory results. The optimal configuration of stages, the number of units in each stage, the unit sizes and minimum total production cost are obtained from the global optimization model and the proposed superstructure. This global model is a mixed integer nonlinear programming (MINLP) formulation, which is represented and resolved by the Professional Software, General Algebraic Modeling System (GAMS) version 23.5 applying DICOPT Solver. Different scenarios are analyzed: attaching pretreatment and enzymatic hydrolysis of bagasse to a conventional distillery plant, selling ethanol, or selling the furfural as by-product if there is a guaranteed market. With this, an actual net present value (VNA) of USD 44´893 358.7 and 1.51 years of Payback Period (PP) are obtained.

Simultaneous design and scheduling of a plant for producing ethanol and derivatives

Abstract In this paper, a mixed integer linear programming model is formulated for the simultaneous design and scheduling of a semicontinuous/batch plant for producing ethanol and two types of yeast. Yeast productions emerge as a sustainable alternative for ethanol residues. The optimal plant configuration, unit sizes, number and size of batches in the campaign and its sequencing is obtained in order to fulfill the ethanol and yeast demands minimizing the investment cost. A novel set of scheduling constraints is proposed for this kind of plants.

A decomposition algorithm for the simultaneous batching and scheduling of multistage batch facilities

Abstract In this work, the simultaneous batching and scheduling problem of multistage batch plants is addressed using mathematical programming. The proposed model can handle multiple orders per product with different due dates, variable processing times, zero-wait transfer policy, and sequence-dependent changeover times. Given the plant configuration, the equipment utilization maximum and minimum capacities and the total required amount of each order, the proposed approach determines the number and size of batches for each customer order, the assignment of batches and their sequence on each unit, and the timing of selected batches, in order to minimize the production and changeover costs. In order to enhance the computational performance of the simultaneous optimization, a solution method based in a decomposition scheme is developed that enables to generate near optimal solutions or the optimal solution of the integrated problem, with reasonable computational effort.