Luis Puigjaner

Incorporating environmental impacts and regulations in a holistic supply chains modeling: An LCA approach

Abstract Corporate approaches to improve environmental performance cannot be undertaken in isolation, so a concerted effort along the supply chain (SC) entities is needed which poses another important challenge to managers. This work addresses the optimization of SC planning and design considering economical and environmental issues. The strategic decisions considered in the model are facility location, processing technology selection and production–distribution planning. A life cycle assessment (LCA) approach is envisaged to incorporate the environmental aspects of the model. IMPACT 2002+ methodology is selected to perform the impact assessment within the SC thus providing a feasible implementation of a combined midpoint–endpoint evaluation. The proposed approach reduces the value-subjectivity inherent to the assignment of weights in the calculation of an overall environmental impact by considering endpoint damage categories as objective function. Additionally, the model performs an impact mapping along the comprising SC nodes and activities. Such mapping allows to focus financial efforts to reduce environmental burdens to the most promising subjects. Furthermore, consideration of CO 2 trading scheme and temporal distribution of environmental interventions are also included with the intention of providing a tool that may be utilized to evaluate current regulatory policies or pursue more effective ones. The mathematical formulation of this problem becomes a multi-objective MILP (moMILP). Criteria selected for the objective function are damage categories impacts, overall impact factor and net present value (NPV). Main advantages of this model are highlighted through a realistic case study of maleic anhydride SC production and distribution network.

Pyrolysis of blends of biomass with poor coals

This paper reports on the pyrolytic behaviour of three different kinds of solid fuel and their blends in order to understand and further improve the performance of fluidized bed gasification of biomass-poor coal blends. Individual feedstocks consisting of pine chips, black coal and waste coal and several biomass-coal blends were pyrolysed in a thermobalance apparatus at atmospheric pressure. Experiments were carried out dynamically by increasing the temperature from 110 to 900°C with a heating rate of 100 K min−1. The data show very poor behaviour of the waste coal compared with pine chips and even black coal. Nevertheless, when a minimum of 40% of pine chips was blended with 60% waste coal and pyrolysed under the same conditions, the rate of mass loss increased sharply and was similar to that of black coal. D.t.g. results indicate that the main pyrolysis processes can be quantitatively expressed in terms of C0 = Cwe−kt for both the individual fuels and the blends. Additional studies showed that no interaction takes place between biomass and coal in a blend during pyrolysis.

Use of neural networks and expert systems to control a gas/solid sorption chilling machine

Abstract This works focuses on using neural networks and expert systems to control a gas/solid sorption chilling machine. In such systems, the cold production changes cyclically with time due to the batchwise operation of the gas/solid reactors. The accurate simulation of the dynamic performance of the chilling machine has proven to be difficult for standard computers when using deterministic models. Additionally, some model parameters dynamically change with the reaction advancement. A new modelling approach is presented here to simulate the performance of such systems using neural networks. The backpropagation learning rule and the sigmoid transfer function have been applied in feedforward, full connected, single hidden layer neural networks. Overall control of this system is divided in three blocks: control of the machine stages, prediction of the machine performance and fault diagnosis.

Simultaneous optimization of process operations and financial decisions to enhance the integrated planning/scheduling of chemical supply chains

Abstract This paper addresses the integrated planning/scheduling of chemical supply chains (SC) with multi-product, multi-echelon distribution networks taking into account financial management issues and suggests a novel approach for enterprise wide management. In order to tackle this problem, it is derived a mathematical formulation combining a scheduling/planning model with a cash flow and budgeting formulation. To motivate the use of such integrated model, a sequential scheme representing traditional enterprise practices is firstly applied. Within this strategy, scheduling and planning decisions are taken firstly, and finances are fitted afterwards considering the cash flows associated to the scheduling/planning decisions previously computed as input parameters. The comparison between the results of the sequential approach and those of the integrated model highlight the advantages of the latter option, in which scheduling/planning and cash management decisions are optimized in unison with a common objective of maximizing the change in equity achieved by the company. The modeling approach developed in this paper and the obtained results suggest that a new conceptual strategy in enterprise management systems consisting of the integration of the financial models of the enterprise with those dealing with the operative area is a must to improve the firm’s performance and its overall earnings and ensuring also healthy cash flow management.

Control and optimization of the divided wall column

The divided wall column (DWC) is, in terms of capital costs and energy savings, a promising alternative for separating ternary mixtures. Since its design was proposed, almost 50 years ago, many authors have addressed design considerations. Operation and control of the DWC have received much less attention. However, some works have been published recently. Preliminary results reported indicate that feedback diagonal control structures may be used to control the DWC. In this work, the study of feedback diagonal control strategies has been further extended to consider the DWC control design in detail. Different control structures have been systematically analyzed and compared under performance and robustness considerations. In order to study the effect of the energy optimization on the controllability of the DWC, a column at optimal nominal operating conditions is compared to a column under non-optimal operation. Finally, a complete control strategy is proposed. Linear analysis tools are used for the multiple input multiple output (MIMO) feedback control analysis, and simulations using a non-linear model are performed to study the non-linear behavior of the control systems.

Capturing dynamics in integrated supply chain management

Abstract A major challenge for an enterprise to stay competitive in today’s highly competitive market environment is to be able of capturing and handling the dynamics of its entire supply chain (SC). This work incorporates uncertainty and process dynamics into enterprise wide models which also contemplate cross-functional decisions. The SC integrated solution developed includes a design–planning and a financial formulations. A model predictive control (MPC) methodology is proposed that comprises a stochastic optimization approach. A scenario based multi-stage stochastic mixed integer linear programming (MILP) model is employed to address the problem. The novel control framework introduced constitutes a step-forward in closing the loop for the dynamic supply chain management (SCM) and a supporting platform for the supervisory module handling the incidences that may arise in the SC. The potential of the presented approach is highlighted through a case study, where the results of the deterministic MPC and the joint control framework are compared. It is emphasized the significance of merging uncertainty treatment and control strategies to improve the SC performance.

Incorporating heat integration in batch process scheduling

This work presents a methodology for incorporating heat integration in batch process scheduling. The formerly introduced S-graph approach [AIChE J. 48 (2002) 2557–2570] for solving multipurpose scheduling problems is proved to be appropriate for the representation of both scheduling problems and the corresponding heat exchanger network synthesis problem. The proposed procedure is based on the branch-andbound framework, where two optimisation problems, the scheduling and the heat integration one, are considered simultaneously instead of consecutively. This method is primarily based on combinatorics and combinatorial algorithms. Solution of several examples illustrates the efficiency of the proposed approach and the benefits of considering heat exchanger network synthesis while scheduling batch processes. � 2003 Elsevier Ltd. All rights reserved.

Optimisation of water use in batch process industries

Abstract The water management in the batch process industries has been optimised considering the reuse of water effluents in the same plant through storage tanks. For a given production plan, the characterisation of the water streams associated to the different production tasks is established. The connections between the available tanks and the water streams have been determined targeting different aspects: freshwater demand, water cost, utility demand of water streams and water reuse network costs. The water reuse system has been modelled, simulated and optimised. A software tool has been developed that permits an easy application of the methodology.

MIP-based decomposition strategies for large-scale scheduling problems in multiproduct multistage batch plants: A benchmark scheduling problem of the pharmaceutical industry

An efficient systematic iterative solution strategy for solving real-world scheduling problems in multiproduct multistage batch plants is presented. Since the proposed method has its core a mathematical model, two alternative MIP scheduling formulations are suggested. The MIP-based solution strategy consists of a constructive step, wherein a feasible and initial solution is rapidly generated by following an iterative insertion procedure, and an improvement step, wherein the initial solution is systematically enhanced by implementing iteratively several rescheduling techniques, based on the mathematical model. A salient feature of our approach is that the scheduler can maintain the number of decisions at a reasonable level thus reducing appropriately the search space. A fact that usually results in manageable model sizes that often guarantees a more stable and predictable optimization model behavior. The proposed strategy performance is tested on several complicated problem instances of a multiproduct multistage pharmaceuticals scheduling problem. On average, high quality solutions are reported with relatively low computational effort. Authors encourage other researchers to adopt the large-scale pharmaceutical scheduling problem to test on it their solution techniques, and use it as a challenging comparison reference.

On-line fault diagnosis system support for reactive scheduling in multipurpose batch chemical plants

Abstract In this work, a simple strategy for the development and implementation of a fault diagnosis system (FDS) that interacts with a schedule optimiser in batch chemical plants is presented. The proposed FDS consists of an artificial neural network (ANN) structure supplemented with a knowledge-based expert system (KBES) in a block-oriented configuration. The system combines the adaptive learning diagnostic procedure of the ANN and the transparent deep knowledge representation of the KBES. The information needed to implement the FDS includes a historical database of past batches, a Hazard and Operability (HAZOP) analysis and a model of the plant. Two motivating case studies are presented to show the results of the proposed methodology. The first corresponds to a fed-batch reactor. In this example, the FDS performance is demonstrated through the simulation of different process faults. The second case study corresponds to a multipurpose batch plant. In this case, the results of reactive scheduling are shown by simulating different abnormal situations. A performance comparison is made against the traditional scheduling approach without the support of the proposed FDS.