Rubén Ruiz-Femenia

An alternative disjunctive optimization model for heat integration with variable temperatures

Abstract This paper presents an alternative model to deal with the problem of optimal energy consumption minimization of non-isothermal systems with variable inlet and outlet temperatures. The model is based on an implicit temperature ordering and the “transshipment model” proposed by Papoulias and Grossmann (1983) . It is supplemented with a set of logical relationships related to the relative position of the inlet temperatures of process streams and the dynamic temperature intervals. In the extreme situation of fixed inlet and outlet temperatures, the model reduces to the “transshipment model”. Several examples with fixed and variable temperatures are presented to illustrate the models performance.

Integration of modular process simulators under the Generalized Disjunctive Programming framework for the structural flowsheet optimization

Abstract The optimization of chemical processes where the flowsheet topology is not kept fixed is a challenging discrete-continuous optimization problem. Usually, this task has been performed through equation based models. This approach presents several problems, as tedious and complicated component properties estimation or the handling of huge problems (with thousands of equations and variables). We propose a GDP approach as an alternative to the MINLP models coupled with a flowsheet program. The novelty of this approach relies on using a commercial modular process simulator where the superstructure is drawn directly on the graphical use interface of the simulator. This methodology takes advantage of modular process simulators (specially tailored numerical methods, reliability, and robustness) and the flexibility of the GDP formulation for the modeling and solution. The optimization tool proposed is successfully applied to the synthesis of a methanol plant where different alternatives are available for the streams, equipment and process conditions.

MINLP-based Analytic Hierarchy Process to simplify multi-objective problems: Application to the design of biofuels supply chains using on field surveys

Abstract Multi-objective optimization (MOO) is widely used in engineering systems design and planning. The solution of a MOO problem leads to a set of efficient points (Pareto set) from which decision-makers should identify the one that best fits their preferences. Generating this set requires large computational efforts, and the post-optimal analysis of the solutions becomes difficult as the number of objectives increases. This work introduces an approach based on the Analytic Hierarchy Process (AHP) to overcome these limitations. Through the definition of an aggregated objective function calculated using the AHP algorithm, a single-objective model is constructed that provides a unique Pareto solution of the original MOO model. The AHP is combined with a mixed-integer non-linear programming (MINLP) formulation that simplifies its application and is particularly suited to deal with many objectives (like those arising in sustainable engineering problems). The capabilities of the approach are demonstrated through a case study addressing the sustainable sugar/ethanol supply chain design problem.

Optimal Design of a Hybrid Membrane System Combining Reverse and Forward Osmosis for Seawater Desalination

Abstract In this work we study Forward Osmosis (FO) as an emerging desalination technology, and its capability to replace totally or partially Reverse Osmosis (RO) in order to reduce the great amount of energy required in the current desalination plants. For this purpose, we propose a superstructure that includes both membrane based desalination technologies, allowing the selection of only one of the technologies or a combination of both of them seeking for the optimal configuration of the network. The optimization problem is solved for a seawater desalination plant with a given fresh water production. The results obtained show that the optimal solution combines both desalination technologies to reduce not only the energy consumption but also the total cost of the desalination process in comparison with the same plant but operating only with RO.

Incorporating CO2 emission trading in the optimal design and planning of chemical supply chain networks under uncertainty

Abstract In this work, we analyze the effect of incorporating the CO 2 emission trading on the optimal design of chemical supply chain (SC) networks considering simultaneously their economic and environmental performance. We present multi-scenario mixed-integer stochastic linear programming (MILP) model with the unique feature of accounting for the effects of CO 2 emissions right cost uncertainty on the economical performance of the network. The uncertain parameter is modeled by a set of scenarios with given probability of occurrence. The environmental performance is quantified following life cycle assessment (LCA) principles, which are explicitly incorporated in the model formulation through standard algebraic equations. The capabilities of the approach presented are illustrated through a case study.

Minimization of the life cycle impact of chemical supply chain networks under demand uncertainty

Abstract In this work, we analyze the effect of demand uncertainty on the multi-objective optimization of chemical supply chain (SC) networks considering simultaneously their economic and environmental performance. We present a novel multi-scenario mixed-integer stochastic linear programming (MILP) model with the unique feature of accounting for the effects of demand uncertainty on the life cycle environmental performance of the network. The uncertain parameter is modeled by a set of scenarios with given probability of occurrence. The environmental performance is quantified following life cycle assessment (LCA) principles, which are explicitly incorporated in the model formulation through standard algebraic equations. The capabilities of the approach presented are illustrated through a case study.

MILP method for objective reduction in multi-objective optimization

Abstract A procedure for reducing objectives in a multi-objective optimization problem given a set of Pareto solutions is presented. Three different models are detailed, which achieve three different degrees of objective reduction. These models are based on maintaining the dominance structure of the problem. To compare the performance of the proposed models, these are tested with pure mathematical cases and with actual data from previous works in the field of multi-objective optimization. The first model provides the reduced subset of objectives that do not alter the dominance structure of the problem at all. The second model determines the minimum subset of objectives that alters the dominance structure with an upper predefined limit for the error. The last model provides the subset of objectives with a previously defined cardinality, which achieves the minimum error. The possibility of different inputs introduces flexibility into the models, which accounts for the preferences of the decision-maker.

Multistage Membrane Distillation for the Treatment of Shale Gas Flowback Water: Multi-Objective Optimization under Uncertainty

Abstract In this work, we analyze the effect of shale gas well data uncertainty on the multi-objective optimization of a multistage direct contact membrane distillation (DCMD) model. The uncertain parameters, flowrate and salt concentration of the flowback water, are modelled by a set of correlated scenarios. A bi-criterion stochastic MINLP was formulated to minimize the expected total annual cost (TAC) and its variability, controlled by the worst case (WC) risk management metric. The model was solved using a modified version of the sample average approximation (SAA) algorithm, which decomposes the original problem into two: a deterministic MINLP model and a stochastic NLP model. The solution is a set of Pareto curves, where the two global extreme solutions provide the DCMD designs that achieve the minimum expected TAC and the minimum WC, respectively. Furthermore, both designs are able to satisfy the zero liquid discharge (ZLD) requirement imposed in the outflow stream.

MILP models for objective reduction in multi-objective optimization: Error measurement considerations and non-redundancy ratio

Abstract A common approach in multi-objective optimization (MOO) consists of removing redundant objectives or reducing the set of objectives minimizing some metrics related with the loss of the dominance structure. In this paper, we comment some weakness related to the usual minimization of the maximum error (infinity norm or δ-error) and the convenience of using a norm 1 instead. Besides, a new model accounting for the minimum number of Pareto solutions that are lost when reducing objectives is provided, which helps to further describe the effects of the objective reduction in the system. A comparison of the performance of these algorithms and its usefulness in objective reduction against principal component analysis + Deb & Saxenas algorithm (Deb & Saxena Kumar, 2005) is provided, and the ability of combining it with a principal component analysis in order to reduce the dimensionality of a system is also studied and commented.

Modelling and optimization framework for the multi-objective design of buildings

Abstract Many energy strategies can be applied to a building to improve its energy efficiency without compromising comfort. The analysis becomes more complex when considering not only the energetic improvement but also the corresponding economic cost and especially when considering its multiple environmental impacts. This study presents a methodology for determining the optimal insulation thickness for external building surfaces. As a case study, we model a cubicle like building. It is representative for a conventional Mediterranean construction system and is situated in Lleida, representing a moderate climate in Spain. Our approach is based on a multi-objective optimization model that minimizes simultaneously the cost and eleven environmental impact indicators associated with both the energy consumption over the operational phase and the construction materials used (including the waste produced during the disposal phase). To simplify the problem we reduce the dimensionality of the multi-objective optimization problem identifying and removing in a systematic manner redundant criteria from the mathematical model. To accelerate the optimization analysis we apply a surrogate model which provides the global prediction of the objective functions, and an evaluation of uncertainty of the prediction of this model. Results show that this approach represents a practical tool for performing the dimensionality reduction of criteria and the acceleration of building design optimization through the use of a surrogate model.