Showing papers by "Ligia Tiruta-Barna published in 2016"
TL;DR: In this paper, two bioenergy production systems of very different complexities were modelled to assess their environmental efficiencies: a biodiesel system and a biogas system, and the cumulative energy demand, Life Cycle Assessment (LCA) and dynamic LCA for climate change were used to evaluate the environmental footprint of the production systems.
55 citations
TL;DR: This paper presents a novel conceptual and computational framework for the consideration of time dependency in LCIs and provides time dependent LCI expressed as: i) time as a function of individual emission (or resource consumption) for individual processes, ii) aggregated time asA function of a given environmental intervention
45 citations
TL;DR: In this paper, the feasibility of coupling rigorous dynamic modelling and its extended boundaries through Life Cycle Assessment (LCA) with an Efficient Multi-Objective Optimization (EMOO) tool was described.
Abstract: The present paper describes a study on the feasibility of coupling rigorous dynamic modelling (DM) and its extended boundaries through Life Cycle Assessment (LCA) with an Efficient Multi-Objective Optimization (EMOO) tool. The combined framework (DM-LCA-EMOO) was then applied to a real-world dynamic system: the wastewater treatment. To give a global view of all environmental, economic and technological performance, three objectives were considered: Effluent Quality Index (EQI), Operational Cost Index (OCI) and environmental impacts quantified through Life Cycle Impact Assessment (LCIA). Legally imposed constraints, including total nitrogen, total phosphorus, total chemical oxygen demand, total suspended solids and ammonium ion were also taken into account. Given the contradictory nature of objectives, the presence of constraints and the time-consuming simulation-based calculations, an Efficient Multi-Objective Optimization framework, namely Archive-based Multi-Objective Evolutionary Algorithm with Memory-based Adaptive Partitioning of search space (AMOEA-MAP) was used. The practicality of such a combined DM-LCA-EMOO tool for the evaluation of wastewater management and treatment was then addressed and demonstrated through a case application.
27 citations
17 citations
TL;DR: The results of optimization reveal that good reduction in both operating cost and environmental impact of the DWPP can be obtained, and NSGA-II outperforms the other competing algorithms while MOEA/D and DE perform unexpectedly poorly.
15 citations
TL;DR: A new framework to solve efficiently MOPs, with a limited computational budget, called AMOEA-MAP is proposed, which relies on the structure of the NSGAII algorithm and possesses two novel operators: a memory-based adaptive partitioning strategy and a bi-population evolutionary algorithm, tailored for expensive optimization problems.
14 citations
01 Jan 2016
TL;DR: A heuristic approach which aims at building a surrogate problem model, solvable by computationally efficient optimization methods, in order to quickly provide a sufficiently accurate estimation of the Pareto front, successfully applied to the cost versus life cycle assessment-based environmental optimization of potable water production plants (PWPPs).
Abstract: Many real-world multi-objective optimization (MOO) problems rely on computationally expensive simulators of industrial processes and require solutions within a limited time budget. In this context, we propose a heuristic approach which aims at building a surrogate problem model, solvable by computationally efficient optimization methods, in order to quickly provide a sufficiently accurate estimation of the Pareto front. The proposed approach generates a multi-objective mixed-integer programming (MO-MIP) proxy model of the MOO problem using sensitivity-based piece-wise linear approximation of objectives and constraints. The approximation of the Pareto front is obtained by applying the ε-constraint method to the multi-objective surrogate problem, transforming it into a desired number of single objective (SO) MIP problems. The paper further explores the pros and cons of three algorithms for the solution of the SO-MIP problems namely constraint programming (CP), MIP, and constraint integer programming (CIP) which integrates CP and MIP methods. In the context of computational sustainability, the proposed methodology is successfully applied to the cost versus life cycle assessment (LCA)-based environmental optimization of potable water production plants (PWPPs). The numerical results obtained indicate that the proposed approach converges much faster to the Pareto front than the state-of-the-art metaheuristic algorithm SPEA2.
1 citations
01 Jan 2016
TL;DR: The paper explores the ability of global sensitivity analysis methods to support MOO of DWPPs by means of a quick identification of a subset of the most sensitive decision variables from a large set, which leads to better quality solutions under limited time budget.
Abstract: In the frameworks of environmental management and computational sustainability, this paper aims at improving, in a cost-effective manner, the sustainability of existing drinking water production plants (DWPPs) via multi-objective constrained optimization (MOO). Specifically, the paper explores the ability of global sensitivity analysis (SA) methods to support MOO of DWPPs by means of a quick identification of a subset of the most sensitive decision variables from a large set, which leads to better quality solutions under limited time budget. To this end, the paper conducts a comparative analysis for this MOO problem of two major SA methods, namely Morris and Sobol’, both provided by the free software SimLab. The numerical results show that, coupled with the meta-heuristic algorithm SPEA2, both SA methods can properly filter out insensitive decision variables, reducing the full set of decision variables by a factor of 3, and improve the Pareto front approximation quality.