Antonino Marvuglia

Application of three independent consequential LCA approaches to the agricultural sector in Luxembourg

PurposeConsequential Life Cycle Assessment (C-LCA) is a “system modelling approach in which activities in a product system are linked so that activities are included in the product system to the extent that they are expected to change as a consequence of a change in demand”. Hence, C-LCA focuses on micro-economic actions linked to macro-economic consequences, by identifying the (marginal) suppliers and technologies prone to be affected by variable scale changes in the demand of a product. Detecting the direct and indirect environmental effects due to changes in the production system is not an easy task. Hence, researchers have combined the consequential perspective with different econometric models. Therefore, the aim of this study is to assess an increase in biocrops cultivation in Luxembourg using three different consequential modelling approaches to understand the benefits, drawbacks and assumptions linked to each approach as applied to the case study selected.MethodsFirstly, a partial equilibrium (PE) model is used to detect changes in land cultivation based on the farmers’ revenue maximisation. Secondly, another PE model is proposed, which considers a different perspective aiming at minimising a total adaptation cost (so-called opportunity cost) to satisfy a given new demand of domestically produced biofuel. Finally, the consequential system delimitation for agricultural LCA approach, as proposed by Schmidt (Int J Life Cycle Assess 13:350–364, 2008), is applied.Results and discussionThe two PE models present complex shifts in crop rotation land use changes (LUCs), linked to the optimisation that is performed, while the remaining approach has limited consequential impact on changes in crop patterns since the expert opinion decision tree constitutes a simplification of the ongoing LUCs. However, environmental consequences in the latter were considerably higher due to intercontinental trade assumptions recommended by the experts that were not accounted for in the economic models. Environmental variations between the different scenarios due to LUCs vary based on the different expert- or computational-based assumptions. Finally, environmental consequences as compared with the current state-of-the-art are lame due to the limited impact of the shock within the global trade market.ConclusionsThe use of several consequential modelling approaches within the same study may help widen the interpretation of the advantages or risks of applying a specific change to a production system. In fact, different models may not only be good alternatives in terms of comparability of scenarios and assumptions, but there may also be room for complementing these within a unique framework to reduce uncertainties in an integrated way.

Learning methods for type-2 FLS based on FCM

This paper presents a new two-phase learning method for interval type-2 fuzzy logic systems. The method combines traditional learning approaches to type-1 fuzzy systems with fitting of interval memberships using FCM memberships. Two improving modifications of the proposed method are supplied additionally.

Some efficient approaches for multi-objective constrained optimization of computationally expensive black-box model problems

Abstract Multi-objective constrained optimization problems which arise in many engineering fields often involve computationally expensive black-box model simulators of industrial processes which have to be solved with limited computational time budget, and hence limited number of simulator calls. This paper proposes two heuristic approaches aiming to build proxy problem models, solvable by computationally efficient optimization methods, in order to quickly provide a sufficiently accurate approximation of the Pareto front. The first approach builds a multi-objective mixed-integer linear programming (MO-MILP) surrogate model of the optimization problem relying on piece-wise linear approximations of objectives and constraints obtained through brute-force sensitivity computation. The second approach builds a multi-objective nonlinear programming (MO-NLP) surrogate model using curve fitting of objectives and constraints. In both approaches the desired number of approximated solutions of the Pareto front are generated by applying the ɛ-constraint method to the multi-objective surrogate problems. The proposed approaches are tested for the cost vs. life cycle assessment (LCA)-based environmental optimization of drinking water production plants. The results obtained with both approaches show that a good quality approximation of Pareto front can be obtained with a significantly smaller computational time than with a state-of-the-art metaheuristic algorithm.

Emergy evaluation using the calculation software SCALE: Case study, added value and potential improvements

This paper reports the emergy-based evaluation (EME) of the ecological performance of four water treatment plants (WTPs) using three different approaches. The results obtained using the emergy calculation software SCALE (EMESCALE) are compared with those achieved through a conventional emergy evaluation procedure (EMECONV), as well as through the application of the Solar Energy Demand (SED) method. SCALEs results are based on a detailed representation of the chain of technological processes provided by the lifecycle inventory database ecoinvent®. They benefit from a higher level of details in the description of the technological network as compared to the ones calculated with a conventional EME and, unlike the SED results, are computed according to the emergy algebra rules. The analysis delves into the quantitative comparison of unit emergy values (UEVs) for individual technospheric inputs provided by each method, demonstrating the added value of SCALE to enhance reproducibility, accurateness and completeness of an EME. However, SCALE cannot presently include non-technospheric inputs in emergy accounting, like e.g. human labor and ecosystem services. Moreover, SCALE is limited by the approach used to build the dataset of UEVs for natural resources. Recommendations on the scope and accuracy of SCALE-based emergy accounting are suggested for further steps in software development, as well as preliminary quantitative methods to account for ecosystem services and human labor.

The Learning of Neuro-Fuzzy Classifier with Fuzzy Rough Sets for Imprecise Datasets

The paper concerns the architecture of a neuro-fuzzy classifier with fuzzy rough sets which has been developed to process imprecise data. A raw output of such system is an interval which has to be interpreted in terms of classification afterwards. To obtain a credible answer, the interval should be as narrow as possible; however, its width cannot be zero as long as input values are imprecise. In the paper, we discuss the determination of classifier parameters using the standard gradient learning technique. The effectiveness of the proposed method is confirmed by several simulation experiments.

The use of temporal dynamics for the automatic calculation of land use impacts in LCA using R programming environment

PurposeEvaluation of soil functionality in Life Cycle Assessment (LCA) has progressively gained importance, although only a small cluster of studies deliver detailed guidelines on how to calculate quantified indicators. In addition, there is a lack of bibliography assessing impacts on the pedosphere due to spatially differentiated land use changes (LUC). In this study, an automated geospatial simulation of LUC based on crop rotation probabilities in Luxembourg was implemented in the programming environment R. Furthermore, this method based on coupling LCA and geographic information system (GIS) was used to calculate changes in soil functionality by implementing both the soil organic carbon (SOC) method and the Land Use Indicator Value Calculation Tool (LANCA®). The developed R script was then applied to a case study dealing with maize production for bioenergy purposes in Luxembourg.MethodsOn the one hand, geo-referenced crop information in Luxembourg for the period 2005–2011 was used to calculate the estimated probability in which crop rotation occurs in combination with maize expansion to meet bioenergy production requirements by 2020. On the other hand, geo-referenced information for a wide range of parameters relevant in assessing soil functionality was stored in a geospatial database and mapped using Rs geospatial data manipulation, analysis and visualisation capabilities. The geospatial data were used as input for the R LANCA® model, which calculates the environmental impacts associated with the five indicators considered in the model (erosion resistance, physicochemical filtration, mechanical filtration, biotic production and groundwater replenishment) for all the cultivated areas in Luxembourg.Results and discussionThe application of the two models demonstrated the significant differences in soil functionality in Luxembourgish arable land, namely between the north and south of the country. Spatial differentiation was found to be important in all indicators, except biotic production and physicochemical filtration, in which the availability of more detailed datasets and more specific methods is a must. Finally, the coupling of GIS and LCI data proved to be an interesting tool for estimating transition probabilities in crop rotation and, therefore, useful in forecasting suitable areas to implement future agricultural policies.ConclusionsGIS and LCI data coupling may constitute an interesting pathway to combine environmental impact assessment and spatial differentiation, provided that further improvements are performed in the method, including important soil parameters or farmer behaviour. In addition, the spatial mapping of environmental impacts can provide important support in terms of policy-making, conservation of natural resources, landscape, or agricultural planning.

A Generalization of the Orthogonal Regression Technique for Life Cycle Inventory

Life cycle assessment (LCA) is a method used to quantify the environmental impacts of a product, process, or service across its whole life cycle. One of the problems occurring when the system at hand involves processes delivering more than one valuable output is the apportionment of resource consumption and environmental burdens in the correct proportion amongst the products. The mathematical formulation of the problem is represented by the solution of an over-determined system of linear equations. The paper describes the application of an iterative algorithm for the implementation of least square regression to solve this over-determined system directly in its rectangular form. The applied algorithm dynamically passes from an Ordinary Least Squares (OLS) problem to the regression problems known as Total Least Squares (TLS) and Data Least Squares (DLS). The obtained results suggest further investigations. In particular, the so called constrained least squares method is identified as an interesting development of the methodology.

Linear programming-based directed local search for expensive multi-objective optimization problems: Application to drinking water production plants

Local search (LS) is an essential module of most hybrid meta-heuristic evolutionary algorithms which are a major approach aimed to solve efficiently multi-objective optimization (MOO) problems. Furthermore, LS is specifically useful in many real-world applications where there is a need only to improve a current state of a system locally with limited computational budget and/or relying on computationally expensive process simulators. In these contexts, this paper proposes a new neighborhood-based iterative LS method, relying on first derivatives approximation and linear programming (LP), aiming to steer the search along any desired direction in the objectives space. The paper also leverages the directed local search (DS) method to constrained MOO problems. These methods are applied to the bi-objective (cost versus life cycle assessment-based environmental impact) optimization of drinking water production plants. The results obtained show that the proposed method constitutes a promising local search method which clearly outperforms the directed search approach.

Cost versus life cycle assessment-based environmental impact optimization of drinking water production plants.

Empowering decision makers with cost-effective solutions for reducing industrial processes environmental burden, at both design and operation stages, is nowadays a major worldwide concern. The paper addresses this issue for the sector of drinking water production plants (DWPPs), seeking for optimal solutions trading-off operation cost and life cycle assessment (LCA)-based environmental impact while satisfying outlet water quality criteria. This leads to a challenging bi-objective constrained optimization problem, which relies on a computationally expensive intricate process-modelling simulator of the DWPP and has to be solved with limited computational budget. Since mathematical programming methods are unusable in this case, the paper examines the performances in tackling these challenges of six off-the-shelf state-of-the-art global meta-heuristic optimization algorithms, suitable for such simulation-based optimization, namely Strength Pareto Evolutionary Algorithm (SPEA2), Non-dominated Sorting Genetic Algorithm (NSGA-II), Indicator-based Evolutionary Algorithm (IBEA), Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D), Differential Evolution (DE), and Particle Swarm Optimization (PSO). The results of optimization reveal that good reduction in both operating cost and environmental impact of the DWPP can be obtained. Furthermore, NSGA-II outperforms the other competing algorithms while MOEA/D and DE perform unexpectedly poorly.

An archive-based multi-objective evolutionary algorithm with adaptive search space partitioning to deal with expensive optimization problems: Application to process eco-design

Abstract In eco-design, the integration of environmental aspects into the earliest stage of design is considered with the aim of reducing adverse environmental impacts throughout a products life cycle. An eco-design problem is therefore multi-objective, where several objectives (environmental, economic, and technological) are to be simultaneously optimized. The optimization of industrial processes usually requires solving expensive multi-objective optimization problems (MOPs). Aiming to solve efficiently MOPs, with a limited computational budget, this paper proposes a new framework called AMOEA-MAP. The framework relies on the structure of the NSGAII algorithm and possesses two novel operators: a memory-based adaptive partitioning strategy, which provides an adaptive reticulation of the search space for a quick identification of optimal zones with less computational effort; and a bi-population evolutionary algorithm, tailored for expensive optimization problems. To ascertain its generality, the framework is first tested on several tough benchmarks. Its performance is subsequently validated on a real-world eco-design problem.