Environmental Objective Functions Research Articles

Synthesis of a sustainable integrated rice mill complex

A rice mill produces many by-products that can be further utilised into value-added products. For instance, rice bran can be further processed to produce rice bran oil. On the other hand, rice husk can be utilised as biomass in the cyclonic husk furnace or cogeneration system to supply thermal energy for paddy drying. Alternatively, rice husk can be the raw material to produce bio-char, bio-oil and biofuel. This paper presents a framework to synthesise the sustainable pathways for an Integrated, Resource-Efficient (IRE) rice mill aimed at maximising its profitability while minimising the environmental impact of its by-product utilisation. The key factors considered include the availability of resources, the cost-effectiveness of the available technology options and the trade-off between profitability and environmental impact such as global warming, photochemical ozone production and eutrophication. The developed model was applied on a case study where five different scenarios representing different economic and environmental objective functions were analysed. The proposed model allows rice millers to target for the maximum profit and synthesise the sustainable process pathways prior to the detailed design of the IRE rice mill complex.

Multi-objective energy management of CHP (combined heat and power)-based micro-grid

In this paper, an IEMS (intelligent energy management system) is proposed for optimal operation of a CHP-based micro-grid over a 24-hour time interval. The IEMS aims to find the optimal set points of DERs (distributed energy resources) and thermal and electrical storage systems, in such a way that the total operation cost and the net emission are minimized, simultaneously. For optimal operation of the micro-grid, a smart ESS (energy storage system) is introduced which consists of electrical and thermal storage systems. A comprehensive mathematical model for the ESS and also the economic and environmental objective functions are formulated. To solve the problem, we propose an efficient MBFO (modified Bacterial Foraging Optimization) algorithm. An interactive fuzzy satisfying method is also used to simulate the trade-off between conflicting objectives (cost and emission). To validate the performance of the proposed algorithm, we compare the MBFO algorithm with some other previously proposed analytical methods. Finally, the proposed IEMS is applied to a micro-grid which consists of various DERs, CHP (combined heat and power) system, ESS and thermal and electrical loads. The results show that the proposed IEMS can smartly cover the total electrical and thermal load demands with respect to economic and environmental criteria.

Application of the multi-objective optimization and risk analysis for the sizing of a residential small-scale CCHP system

Multi-objective optimization for sizing of a small-scale combined cooling, heating, and power generation (CCHP) system is performed. In multi-objective optimizing the s CCHP system, the three objective functions including the exergetic efficiency, total levelized cost rate of the system product and the cost rate of environmental are optimized, simultaneously. The environmental impact and thermoeconomic objective are minimized while the exegetic objective is maximized. A comprehensive emission assessment framework suitable for addressing of distributed cogeneration systems is formulated according to an electrical output-based emission factor approach and the environmental impact objective function are defined and expressed in cost terms. The economic analysis is conducted in accordance with the total revenue requirement (TRR) method. The genetic algorithm is applied to find the set of Pareto optimal solutions with respect to the aforementioned objective functions. In the present work, reliability and availability are introduced in the developed models of the system, so that redundancy is embedded in the optimal solution. In this regard, risk analysis is used as a decision-making tool for the selection of the final optimal solution from the obtained Pareto optimal frontier. The sensitivity of the optimal solution respect to variations of input parameters is analyzed.

Multi-objective approach in thermoenvironomic optimization of a small-scale distributed CCHP system with risk analysis

Multi-objective optimization for designing of a small-scale distributed CCHP system has been performed. Small-scale combined cooling, heating, and power generation technologies represent a key resource to increase generation efficiency and reduce greenhouse gas emissions with respect to conventional separate production means. In the multi-objective optimization of the small-scale distributed CCHP system, the three objective functions including the exergetic efficiency, total levelized cost rate of the system product and the cost rate of environmental impact have been considered. The environmental impact objective function has been defined and expressed in cost terms however this objective has not been integrated with the thermoeconomic objective. The thermodynamic modeling has been implemented comprehensively while the economic analysis conducted in accordance with the total revenue requirement (TRR) method. One of the most suitable optimization techniques namely as genetic algorithm has been applied to find the set of Pareto optimal solutions with respect to the aforementioned objective functions. In the present work, reliability and availability are introduced in the thermoenvironomic model of the system, so that redundancy is embedded in the optimal solution. Risk analysis is used for decision-making of the final optimal solution from the obtained Pareto optimal frontier.

Multiobjective synthesis of heat exchanger networks minimizing the total annual cost and the environmental impact

Abstract This paper presents a mathematical programming formulation for the synthesis of heat exchanger networks minimizing simultaneously the total annual cost and the environmental impact. The proposed model consists of a Multiobjective Mixed Integer Non-Linear programming problem that considers the optimal location and use of different types of hot and cold utilities available through disjunctive formulations. The total annual cost objective function considers the minimization of the cost for the utilities and capital for the heat exchanger units, whereas the environmental impact objective function is calculated through the eco-indicator 99 for the different types of utilities using the life cycle analysis methodology. Three example problems are presented to show the applicability of the proposed methodology without numerical complications.

A property‐based approach to the synthesis of material conservation networks with economic and environmental objectives

AbstractThis article presents a multiobjective optimization model for the recycle and reuse networks based on properties while accounting for the environmental implications of the discharged wastes using life‐cycle assessment. The economic objective function considers fresh sources and treatment costs, whereas the environmental objective function is measured through the eco‐indicator 99. The model considers constraints in the process sinks as well as in the environment based on stream properties such as pH, chemical oxygen demand, toxicity, density, and color, in addition to the composition of the waste streams. A global optimization procedure is developed by indirectly tackling properties through property operators and by segregating the process streams before treatment. Three examples are included, and the results show that it is possible to consider simultaneously the trade‐offs between the total annual costs and the overall environmental impact using the proposed methodology. © 2010 American Institute of Chemical Engineers AIChE J, 2011

Airspace Constraints in Aircraft Emission Trajectory Optimization

This paper describes a method for handling restricted airspace in trajectory optimization problems while maintaining the full dynamics of the aircraft model. The discussion is limited to the local solution of the optimization problem. The topological problem of determining which side of the restricted regions the aircraft trajectory should take can be seen as more of a preprocessing stage that determines, for example, the shortest path. The trajectory optimization is performed with environmental objective functions describing the emissions from the aircraft engine. Results from two cases are presented. The first case is flying in the vicinity of an airport during the approach and avoiding flying directly above urban areas. The second case involves a long-distance flight with a large region of restricted airspace in the way. Both cases are performed with a model of the Swedish Air Force trainer SK60. The results show that the solution and the solution time significantly depend on the initial starting guess. With a feasible starting guess, the efficiency of the optimization algorithm is not too degraded by the nonconvex airspace constraints.

Evaluating the Environmental Impacts of Water Distribution Systems by Using EIO-LCA-Based Multiobjective Optimization

Climate change has made environmental impact a factor of growing importance in decision making for municipalities. Increasingly, the environmental impacts of expanding and operating a water distribution system (WDS) are considered alongside the cost and hydraulic design. This paper presents a nondominated sorting genetic algorithm (NSGA-II) that minimizes capital costs, annual pumping energy use, and environmental impacts in WDS design that adheres to hydraulic constraints. A previously developed environmental impact (EI) index is included in the environmental objective function of the optimization program. The EI index normalizes and aggregates multiple environmental measures evaluated with an economic input-output life-cycle assessment (EIO-LCA) model. The EIO-LCA-based NSGA-II was applied to the Anytown network. Annual pumping energy use was found to dominate the EI index while capital cost and the EI index were inversely related, and the annual pumping energy use and the EI index followed a near linea...

Optimización Termoeconómica y Ambiental usando Algoritmos Genéticos Multiobjetivo

This paper presents a multiobjective analysis including variables of the environment of the energy system which is conditioned by the use of solid fuels, for this reason the analysis includes the external characteristics of the resource (fuel) of residue (ash) and the product (electricity/steam). The energy system used is Termocesar, a Colombian process for generating electricity from carbon. Three functions named thermoeconomic objective function, technology objective function and environmental objective function, are analyzed to establish the best location of the energy The genetic algorithm method is used for the optimization of the system. The results were analyzed for nine combinations of weights and show that for most of the cases the optimum includes coals from the Department of Cesar in Colombia.

Economic, environmental and mixed objective functions in non-linear process optimization using simulated annealing and tabu search

Screening of topologies developed by hierarchical heuristic procedures can be carried out by comparing their optimal performance. In this work we will be exploiting mono-objective process optimization using two algorithms, simulated annealing and tabu search, and four different objective functions: two of the net present value type, one of them including environmental costs and two of the global potential impact type. The hydrodealkylation of toluene to produce benzene was used as case study, considering five topologies with different complexities mainly obtained by including or not liquid recycling and heat integration. The performance of the algorithms together with the objective functions was observed, analyzed and discussed from various perspectives: average deviation of results for each algorithm, capacity for producing high purity product, screening of topologies, objective functions robustness in screening of topologies, trade-offs between economic and environmental type objective functions and variability of optimum solutions.

Multi-objective approach in thermoenvironomic optimization of a benchmark cogeneration system

Multi-objective optimization for designing of a benchmark cogeneration system known as CGAM cogeneration system has been performed. In optimization approach, the exergetic, economic and environmental aspects have been considered, simultaneously. The thermodynamic modeling has been implemented comprehensively while economic analysis conducted in accordance with the total revenue requirement (TRR) method. The results for the single objective thermoeconomic optimization have been compared with the previous studies in optimization of CGAM problem. In multi-objective optimization of the CGAM problem, the three objective functions including the exergetic efficiency, total levelized cost rate of the system product and the cost rate of environmental impact have been considered. The environmental impact objective function has been defined and expressed in cost terms. This objective has been integrated with the thermoeconomic objective to form a new unique objective function known as a thermoenvironomic objective function. The thermoenvironomic objective has been minimized while the exergetic objective has been maximized. One of the most suitable optimization techniques developed using a particular class of search algorithms known as multi-objective evolutionary algorithms (MOEAs) has been considered here. This approach which is developed based on the genetic algorithm has been applied to find the set of Pareto optimal solutions with respect to the aforementioned objective functions. An example of decision-making has been presented and a final optimal solution has been introduced. The sensitivity of the solutions to the interest rate and the fuel cost has been studied.

Energy, economy and environment as objectives in multi-criterion optimization of thermal systems design

The paper shows how a thermal system design can be optimized using energy, economy and environment as separate objectives. Comparisons with a single-objective thermo-economic optimization and a two-objective energetic and economic optimization are also discussed. The test case plant of the CGAM problem is taken as an example of application for the three-objective approach. An environmental impact objective function is defined and expressed in cost terms by weighting carbon dioxide and nitrogen oxide emissions according to their unit damage costs. An evolutionary algorithm is used to find the surface of optimal solutions in the space defined by the three objective functions.

The application of life cycle assessment to process optimisation

One of the main potential uses of life cycle assessment (LCA) in environmental management is for identifying options for environmental improvements of a system in which complete supply chains are considered. The main problem, however, lies in finding the optimum improvement strategies and choosing the best alternative in a decision environment with multiple, and often conflicting, objectives. To aid the decision-making process, this paper proposes the use of multiobjective optimisation (MO), whereby the system is simultaneously optimised on a number of environmental objective functions, defined and quantified through the LCA approach. This results in a Pareto or noninferior surface, with a range of environmental optima, from which the best compromise solution for improving the environmental performance of the system can be chosen. However, system improvements cannot be based solely on environmental considerations and other factors, including socio-economic, must be considered in parallel. This paper also shows that MO coupled with LCA provides a powerful tool for balancing environmental and economic performance, thus enabling the choice of best practicable environmental option (BPEO) and best available technique not entailing excessive cost (BATNEEC). The value of this approach in environmental system analysis lies in providing a set of alternative optimal options for system improvements rather than a single prescriptive solution, which may be optimal but not necessarily appropriate for a particular situation. A decision–aid tool–optimum LCA performance (OLCAP)–has been developed for these purposes. OLCAP is tested and demonstrated by application to a case study of an existing mineral-processing system producing boron products. It is shown that LCA can successfully be combined with optimisation techniques to satisfy both economic and environmental criteria for more sustainable performance of the product system over the whole life cycle.

Life cycle assessment and multiobjective optimisation

Life cycle assessment (LCA) is a method to identify and quantify the environmental performance of a process or a product from “cradle to grave”. Its main potential in environmental decision-making lies in providing a quantitative basis for assessing potential improvements in environmental performance of a system throughout the life cycle. This paper introduces the use of multiobjective system optimisation in LCA as a tool for identifying and evaluating the best possible options for environmental management of the product system. A life cycle of a system is optimised on a number of environmental objective functions, defined in terms of the usual LCA burden or impact categories, and a range of environmental optima is found on the Pareto or non-inferior surface. As a result, possibilities for improving the environmental performance of the system are identified. Since system improvements cannot be carried out on the basis of environmental LCA only, it is also shown in this paper that the compromise between environmental and economic performance can be found on the non-inferior surface. The value of multiobjective optimisation in system analysis lies in providing a set of alternative options for system improvements rather than a single prescriptive solution, thus enabling the choice of the Best Practicable Environmental Option (BPEO) and Best Available Technique Not Entailing Excessive Cost (BATNEEC). This approach is illustrated by application to a real case study of a system producing five borate products.

Linear programming as a tool in life cycle assessment

Linear Programming (LP) is a powerful mathematical technique that can be used as a tool in Life Cycle Assessment (LCA). In the Inventory and Impact Assessment phases, in addition to calculating the environmental impacts and burdens, it can be used for solving the problem of allocation in multiple-output systems. In the Improvement Assessment phase, it provides a systematic approach to identifying possibilities for system improvements by optimising the system on different environmental objective functions, defined as burdens or impacts. Ultimately, if the environmental impacts are aggregated to a single environmental impact function in the Valuation phase, LP optimisation can identify the overall environmental optimum of the system. However, the aggregation of impacts is not necessary: the system can be optimised on different environmental burdens or impacts simultaneously by using Multiobjective LP. As a result, a range of environmental optima is found offering a number of alternative options for system improvements and enabling the choice of the Best Practicable Environmental Option (BPEO). If, in addition, economic and social criteria are introduced in the model, LP can be used to identify the best compromise solution in a system with conflicting objectives. This approach is illustrated by a real case study of the borate products system.

An efficient budget allocation policy for decentralisation of responsibility for site decontamination projects

Selection and execution of site decontamination projects is often best left to local authorities, in accordance with the subsidiarity principle, even though the budget for such projects is made available through a central authority. In this paper we suggest a practical budget allocation policy which a central authority can employ to allocate budgets to local authorities, while still optimising the central authority's environmental objective function. The procedure is fully consistent with the principle of decentralisation of responsibility for selection and execution of projects, and requires a minimum information exchange between local and central levels. Despite the information asymmetry between local and central levels, incentive compatibility problems can be (partially) prevented by choosing an appropriate evaluation mechanism. At the same time, the procedure is computationally effective and efficient, and can guarantee a fair budget allocation, making it easy to implement and politically acceptable.