Multi-objective optimisation of bio-based thermal insulation materials in building envelopes considering condensation risk

The reduction in energy demand for heating and cooling with insulation materials increases the material related environmental impact. Thus, implementing low embodied energy materials may equilibrate this trade-off. Actual trends in passive house postulate bio-based materials as an alternative to conventional ones. Despite that, the implementation of those insulators should be carried out with a deeper analysis due to their hygroscopic properties. The moisture transfer, the associated condensation risk and the energy consumption for seven biobased materials and polyurethane for a building-like cubicle are analysed. The performance is evaluated combining a software application to model the cubicle (EnergyPlus) and a tool to optimize its performance (jEPlus). The novelty of this optimization approach is to include and evaluate the effects of moisture in these insulation materials, taking into account the mass transfer through the different layers and the evaporation of the different materials. This methodology helps optimise the insulation type and thickness verifying the condensation risk, preventing the deterioration of the materials. The total cost of the different solutions is quantified, and the environmental impact is determined using the life cycle assessment methodology. The effect of climate conditions and the envelope configuration, as well as the risk of condensation, are quantified. The results show that cost and environmental impact can be reduced if bio-based materials are used instead of conventional ones, especially in semiarid climates. Condensation risk occurs for large thicknesses and in humid climates. In our case studies, hemp offered the most balanced solution.The authors would like to acknowledge financial support from the Spanish Government (CTQ2016-77968-C3-1-P, ENE2015-64117-C5-1-R, ENE2015-64117-C5-3-R, MINECO/FEDER, UE). The research leading to these results has received funding from the European Commission Seventh Framework Programme under grant agreement no. PIRSES-GA-2013-610692 (INNOSTORAGE). This project has received funding the European Union's Horizon 2020 Research and Innovation Program under grant agreement No 657466 (INPATH-TES). This article has been possible with the support of the Ministerio de Economía y Competitividad (MINECO) and the Universitat Rovira i Virgili (URV) (FJCI-2016-28789). Authors would like to acknowledge the Brazilian Government for their support by the CNPq (National Council for Scientific and Technological Development). M.P. would like to thank the Brazilian Education Ministry for the financial support received under the PNPD/Capes fellowship. L.F.C. would like to thank the Catalan Government for the quality accreditation given to her research group GREA (2014 SGR 123)

Non-linear Robust Identification of a Greenhouse Model using Multi-objective Evolutionary Algorithms

[EN] This paper presents a non-linear climatic model (temperature and humidity), based on first-principles equations, of a greenhouse where roses are to be grown using hydroponic methods. Fitting of model parameters (15 in all) is based on measured data collected during summer in the Mediterranean area. A multi-objective optimisation procedure for estimating a set of non-linear models Theta(P) (Pareto optimal), considering simultaneously several optimisation criteria, is presented. A new multi-objective evolutionary algorithm, (sic)-MOGA, has been designed to converge towards ((Theta) over cap (P)* a reduced but well distributed representation of Theta(P) since good convergence and distribution of the Pareto front J(Theta(P)) is achieved by the algorithm. The set can (Theta) over cap (P)* be used as the basis to choose an optimal model that offers a good trade-off among different optimality criteria that have been established. The procedure proposed is applied to the identification and validation of the greenhouse model presented in the paper. (C) 2007 IAgrE. Published by Elsevier Ltd. All rights reserved.Partially supported by MEC (Spanish government) and FEDER funds: projects DPI2005-07835 and DPI2004-8383-C03-02, and Generalitat Valenciana GV06/026. We would like to thank the R&D+i Linguistic Assistance Office at the Universidad Polite´cnica de Valencia for their help in translating this paper.Herrero Durá, JM.; Blasco, X.; Martínez Iranzo, MA.; Ramos Fernández, C.; Sanchís Saez, J. (2007). Non-linear Robust Identification of a Greenhouse Model using Multi-objective Evolutionary Algorithms. Biosystems Engineering. 98(3):335-346. https://doi.org/10.1016/j.biosystemseng.2007.06.004S33534698

Multiobjective optimization of water distribution systems accounting for economic cost, hydraulic reliability, and greenhouse gas emissions

In this paper, three objectives are considered for the optimization of water distribution systems (WDSs): the traditional objectives of minimizing economic cost and maximizing hydraulic reliability and the recently proposed objective of minimizing greenhouse gas (GHG) emissions. It is particularly important to include the GHG minimization objective for WDSs involving pumping into storages or water transmission systems (WTSs), as these systems are the main contributors of GHG emissions in the water industry. In order to better understand the nature of tradeoffs among these three objectives, the shape of the solution space and the location of the Pareto-optimal front in the solution space are investigated for WTSs and WDSs that include pumping into storages, and the implications of the interaction between the three objectives are explored from a practical design perspective. Through three case studies, it is found that the solution space is a U-shaped curve rather than a surface, as the tradeoffs among the three objectives are dominated by the hydraulic reliability objective. The Pareto-optimal front of real-world systems is often located at the "elbow" section and lower "arm" of the solution space (i.e., the U-shaped curve), indicating that it is more economic to increase the hydraulic reliability of these systems by increasing pipe capacity (i.e., pipe diameter) compared to increasing pumping power. Solutions having the same GHG emission level but different cost-reliability tradeoffs often exist. Therefore, the final decision needs to be made in conjunction with expert knowledge and the specific budget and reliability requirements of the system. © 2013. American Geophysical Union. All Rights Reserved.Wenyan Wu, Holger R. Maier, and Angus R. Simpso

Life-cycle optimization of building performance: a collection of case studies

The building sector is one of the most impacting on the energy demand and on the environment in developed countries, together with industry and transports. The European Union introduced the topic of nearly zero-energy building (nZEB) and promoted a deep renovations in the existing building stock with the aim of reducing the energy consumption and environmental impacts of the building sector. The design of a nZEB, and in general of a low-energy building, involves different aspects like the economic cost, the comfort indoor, the energy consumption, the life cycle environmental impacts, the different points of view of policy makers, investors and inhabitants. Thus, the adoption of a multicriteria approach is often required in the design process to manage some potential conflicting domains. In detail, one of the most suitable approaches is to integrate the preliminary building design (or renovation) phase in a multi-objective optimization problem, allowing to rapidly compare many alternatives and to identify the most adapt interventions

Optimal greenhouse cultivation control: survey and perspectives

Abstract: A survey is presented of the literature on greenhouse climate control, positioning the various solutions and paradigms in the framework of optimal control. A separation of timescales allows the separation of the economic optimal control problem of greenhouse cultivation into an off-line problem at the tactical level, and an on-line problem at the operational level. This paradigm is used to classify the literature into three categories: focus on operational control, focus on the tactical level, and truly integrated control. Integrated optimal control warrants the best economical result, and provides a systematic way to design control systems for the innovative greenhouses of the future. Research issues and perspectives are listed as well

A Compatible Control Algorithm for Greenhouse Environment Control Based on MOCC Strategy

Conventional methods used for solving greenhouse environment multi-objective conflict control problems lay excessive emphasis on control performance and have inadequate consideration for both energy consumption and special requirements for plant growth. The resulting solution will cause higher energy cost. However, during the long period of work and practice, we find that it may be more reasonable to adopt interval or region control objectives instead of point control objectives. In this paper, we propose a modified compatible control algorithm, and employ Multi-Objective Compatible Control (MOCC) strategy and an extant greenhouse model to achieve greenhouse climate control based on feedback control architecture. A series of simulation experiments through various comparative studies are presented to validate the feasibility of the proposed algorithm. The results are encouraging and suggest the energy-saving application to real-world engineering problems in greenhouse production. It may be valuable and helpful to formulate environmental control strategies, and to achieve high control precision and low energy cost for real-world engineering application in greenhouse production. Moreover, the proposed approach has also potential to be useful for other practical control optimization problems with the features like the greenhouse environment control system

Economic and Environmental Optimization in the Supply of Switchgrass in Tennessee

The low efficiency of collection, storage and transportation in the switchgrass supply chain has hindered the commercialization of a switchgrass-based biofuel industry, even given its ecological and environmental advantages in carbon sequestrate, soil quality, water use, and pollution pressure. Thus, designing a switchgrass-based supply chain balancing both environmental and economic performance is important to expedite the development of the cellulosic biofuel industry to meet the national energy plan. The objectives of this study are to 1) determine economic cost and multiple environmental outcomes in feedstock supply chains and 2) identify the relation between the economic and environmental performances. The first paper considers three objectives: minimization of economic cost, greenhouse gas (GHG) emissions, and soil erosions. The second paper focuses on the relation between economic cost and abated greywater footprint for industrialized supply of cellulosic biofuel in west Tennessee. The improved augmented epsilon method and compromise solution method were applied to high-resolution spatial data to determine the optimal placement of the feedstock supply chains. Results in the first paper indicated that land change into switchgrass production is crucial to both plant-gate cost and environmental impact of feedstock supply. Converting croplands to switchgrass incurred higher opportunity cost from land use change but stored more soil carbon and generated less soil erosion. Tradeoffs in higher feedstock costs with lower GHG emissions and lower soil erosion on the frontier were captured. Soil erosion was found more cost effective criterion than GHG emission in general. The compromise solution location for the conversion facility generated at 63% increase in feedstock cost but improved the environmental impact in lowering 27 % GHG emission and decreasing soil erosion by 70 times lower in the feedstock supply chain compared with cost minimization location. Results in the second paper showed that tradeoff between feedstock costs and greywater footprint was mainly associated with the changes of land use, while ambient water quality condition was also influential to the selection of feedstock production area. The average imputed cost of lowering grey water footprint in the most preferred feedstock supply chain in west Tennessee was $0.94 m-3 [per cubic meter]

Multi crteria decision making and its applications : a literature review

This paper presents current techniques used in Multi Criteria Decision Making (MCDM) and their applications. Two basic approaches for MCDM, namely Artificial Intelligence MCDM (AIMCDM) and Classical MCDM (CMCDM) are discussed and investigated. Recent articles from international journals related to MCDM are collected and analyzed to find which approach is more common than the other in MCDM. Also, which area these techniques are applied to. Those articles are appearing in journals for the year 2008 only. This paper provides evidence that currently, both AIMCDM and CMCDM are equally common in MCDM