Multi-objective Optimization Tool Research Articles

The water energy food nexus: A multi-objective optimization tool

Many methods and tools are used to model and optimize various aspects of the water energy food nexus (WEFN). This work presents a novel software tool for the multi-objective optimization of the WEFN (MOO-WEFN). The model underlying the tool captures all three nexus sectors and the interlinkages between them. In addition, the model imposes constraints that mimic forces affecting the nexus, such as economics, environment, health, and nutrition. The software tool couples Excel with Python, which facilitates the tool's accessibility and ease of use due to its widespread usage and availability. A case study application demonstrates the tool's execution and provides examples of how changes can be made to the data to simulate different nexus developments. Five scenarios are applied to reflect such changes with results demonstrating the overall impact on resource production and consumption. Removing one of the less water-intensive food items results in a 20% increase in water consumption. Limiting the availability of surface water results in a reallocation of water resources such that 80% of water needs are satisfied from surface water and groundwater and 20% from wastewater, compared to the baseline of 100% surface water. Moreover, upon introduction of the conventional activated sludge treatment technology for wastewater (compared to the incumbent membrane bioreactor technology), energy consumption decreased by 10%. These results show changes beyond the immediate sector of change and reflect the necessity for integrated modeling that captures the impact of one change on the nexus as a whole, in terms of resource consumption and allocation. Thus, the tool supports the capability of testing how new resources, technologies, and limitations will impact nexus interactions. Furthermore, the tool facilitates carrying out sensitivity and Pareto analyses for data generation and result evaluation.

Multi-objective optimization of a novel dynamic concentrated skin system considering daylight, building skin capacity, and visual space

Transmissive concentrator technology can solve the problems of expression of building skin form, maximized use of solar energy, and natural lighting inside the building that arise from integrating traditional photovoltaic technology. However, the advantages mentioned above interact and cannot be maximized to solve multiple problems simultaneously. Therefore, it becomes crucial to address the relationship between them optimally and improve the quality of the building and the environment. This study used grasshopper, ladybug, and multi-objective optimization tools to develop and analyze a transmission concentrator module integrated into an office building skin. An optimization framework for dynamic concentrating skins is constructed by modifying important design variables, resolving conflicting relationships between energy output, daylight quality, and visual space, and computing optimal solutions to multiple problems. The results show that the concentrated skin scheme, compared with the common skin and no skin scheme, can reduce the likelihood of glare by an average of 59.35% and 88.40%, respectively, and improve the useful daylight illuminance by an average of 2.40% and 16.25%, respectively, while providing the user with a visual field rate of about 80.54%, in the presence of higher energy output. In addition, thanks to the variation of the concentrating skin variables, using the recommended value of optimal interval and the most significant variables can better resolve the relationship between environment and energy and obtain optimal overall performance and solutions. This study provides data and methods for the integrating design of concentrating skin and optimizing indoor daylighting, which is expected to reduce carbon emissions.

Multi-objective decision-making for green infrastructure planning: Impacts of rainfall characteristics and infrastructure configuration

Rapid urbanization has caused significant water-related problems in urban areas, including flooding and pollution. Green infrastructure has emerged as an effective solution within the realm of nature-based approaches to address these problems. However, there is a need for multi-objective decision-making in green infrastructure planning to strike a balance among various benefits, including those related to the anthroposphere and hydrosphere. Unfortunately, the impacts of rainfall characteristics and infrastructure configuration on multi-objective optimization outcomes are not well understood. To bridge this knowledge gap, a multi-objective optimization tool was developed in this study considering area, location, and hydrological linkages across green infrastructures. This tool combines the Storm Water Management Model (SWMM) and the Strength Pareto Evolutionary Algorithm 2. The proposed tool was implemented in a district-scale research region characterized by seven different rainfall patterns and four return periods. The results show that the proposed tool, integrated with the calibrated and validated SWMM, can provide rational configuration solutions for the target region. Rainfall characteristics significantly affect the cost-efficiency curve and layout of the green infrastructure, given their diverse nature. Solutions optimized by this approach yield optimal economic and environmental benefits. Notably, solutions incorporating hydrological linkages exhibit reduction rates approximately twice as high as those without such linkages. These findings may provide a workable theoretical basis for nature-based solutions and valuable guidance for urban water management.

An efficient and invertible machine learning-driven multi-objective optimization architecture for light olefins separation system

The transience, cyclicity, and flexibility of process operation make the optimization of light olefins separation (LOS) system computationally intensive. An efficient and invertible architecture by incorporating machine learning techniques into optimization routines is proposed. Initially, a steady-state model of the LOS system was built allowing verification with industrial data and invocation through a COM interface. Then, artificial neuron networks (ANNs) were implemented to establish surrogate models for individual fitness calculation. Finally, to find the optimal operating parameters under the targets of energy and economy, an improved non-dominated sorting genetic algorithm was employed along with the ANN-based surrogate models. Optimization results demonstrates that the proposed architecture provides much better performance of computational efficiency with only 0.8 h, while the traditional surrogate models take more than 53 h. Furthermore, the reintroduction of optimized parameters into the LOS system model constructed by Aspen Plus V12.1 demonstrates the satisfactory fulfillment of the separation requirements and affirms the excellent invertibility of the proposed architecture. Essentially, it has been proved to be a powerful tool for multi-objective optimization that can be applied to large-scale chemical processes to derive insights for sustainable development and clean production.

A multi-objective cooperation search algorithm for cascade reservoirs operation optimization considering power generation and ecological flows

With growing attention focused on energy generation and environmental conservation, the operation of cascade reservoirs that considers power generation benefits and ecological flow requirements plays an increasingly important role in water resource and power systems. However, the traditional optimization methods may fail to solve complex cascade reservoirs operation models with strong spatial-temporal coupling physical constraints. To effectively address this problem, this study proposes an efficient multi-objective cooperation search algorithm (MOCSA) that utilizes the modified team communication, reflective learning operator and internal competition operators to enhance global exploration and local exploitation. MOCSA is tested on a group of multi-objective benchmark functions and real-world constrained engineering problems. The results demonstrate that MOCSA can provide better results for most benchmark test functions in comparison with the competitive algorithms. Besides, MOCSA exhibits excellent search ability to find feasible solutions of constrained engineering problems. The proposed method is then applied to resolve a real-world reservoir system under different operation scenarios. Simulations indicate that MOCSA provides diversified decision options for the operation of cascade reservoir system and find a more diverse set of non-dominated solutions within the feasible solution space. Overall, this research presents MOCSA as an effective multi-objective optimization tool for complex cascade reservoir operation problems.

Direct reduction of pellets through hydrogen: Experimental and model behaviour

This paper presents the hydrogen reduction behaviour of industrial pellets designed for the efficient hydrogen based direct reduction. The pellets were provided with very low non ferrous oxides percentage (0.52 of basicity index) and with the absence on TiO2 oxides. The pellets measured diameters in the range 1.14–1.72 cm and were characterized in terms of porosity, pores size, tortuosity and compression strength. The pellets were reduced in hydrogen atmosphere in a laboratory shaft furnace in the temperature ranges of 600–1200 °C at the pressures of 1 and 5 bar. The pellets' reduction behaviour was analysed in terms of time to reduction, rate of reduction and kinetics constant. All the obtained results were analysed through the employment of a commercial multi-objective optimization tool (modeFrontier) in order to precisely define the effect of each single parameter on the pellets’ reduction. It was also defined the effect of the ongoing reduction rate of the final metallization of the starting iron oxides.

Multi-objective battery energy storage optimization for virtual power plant applications

The increasing share of renewable energy sources (RESs) in electricity generation leads to increased uncertainty of generation, frequency and voltage regulation as well as difficulties in energy management. A virtual power plant (VPP), as a combination of dispersed generator units, controllable load and energy storage system (ESS), provides an efficient solution for energy management and scheduling, so as to reduce the cost and network impact caused by the load spikes. This paper proposes a multi-objective optimization (MOO) of battery energy storage system (BESS) for VPP applications. A low-voltage (LV) network in Alice Springs (Northern Territory, Australia) is considered as the test network for this study. The BESS for each customer is used to store and release the energy when required to maintain the voltage regulation performance of the LV network and reduce the cost. To optimize the charge/discharge schedule in each battery, a multi-objective optimization tool (MOOT) is developed, where MOO can directly communicate with DIgSILENT PowerFactory platform to perform multi-time scale simulation. MOO mainly focuses on generating a set of trade-off schedule solutions for each customer over 24 h by considering the customer’s cost and network impact. Then according to the stakeholder’s prior preference, one of the solutions is selected and verified in DIgSILENT PowerFactory with a realistic LV network. We also design several scenarios with different penetrations of photovoltaics (PVs) and batteries to verify how they influence customer’s cost and LV network. With the increasing penetrations of PVs and batteries, the experimental results over the selected solution according to the customer preference show that the customers’ cost can be largely reduced by maintaining the network voltage regulation performance.

Techno-economic optimization and Nox emission reduction through steam injection in gas turbine combustion chamber for waste heat recovery and water production

Considering the persistent human need for electricity and fresh water, cogeneration systems based on the production of these two products have attracted the attention of researchers. This study investigates a cogeneration system of electricity and fresh water based on gas turbine (GT) as the prime mover. The wasted energy of the GT exhaust gases is absorbed by a heat recovery steam generator (HRSG) and supplies the superheat steam required by the steam turbine (ST). In order to produce fresh water, a multi-effect desalination (MED) system is applied. The motive steam required is provided by extracting steam from the ST. In order to reduce the environmental pollution of this cogeneration system, the steam injection method is proposed in the GT's combustion chamber (CC). This system is optimized by a multi-objective optimization tool based on the Genetic Algorithm (GA). The design variables include pressure ratio of compressor (CPR), inlet temperature of gas turbine (TIT), steam injection mass flow rate in the CC, HRSG operating pressure, HRSG evaporator pinch point temperature difference (PPTD), steam pressure of the MED ejector, ejector motive steam flow rate, number of MED effects, and return effect. The goals are to minimize the total cost rate (TCR), which includes the cost of initial investment and maintenance of the system, the cost of consumed fuel, and the cost of disposing of CO and NO pollutants, as well as maximizing the exergy efficiency. In the end, it is observed that the steam injection in the CC leads to the reduction of the mentioned pollutant index, and it is proposed as a suitable solution to reduce the pollution of the proposed cogeneration system.

New optimized configuration for a hybrid PVT solar/electrolyzer/absorption chiller system utilizing the response surface method as a machine learning technique and multi-objective optimization

The presented study examined the multigenerational power, cooling, heating, and hydrogen systems in coastal areas using thermodynamic and economic modeling and multiobjective optimization tools. The organic Rankine cycle (ORC), water heater, proton-exchange membrane (PEM) electrolyzer, absorption chiller, and photovoltaic (PV/T) panel were all included in a case study that was carried out in four different regions. In the EES, the suggested system was thermodynamically modeled. By utilizing the surface response (RSM) methodology, multi-objective optimization was carried out to identify the ideal PV/T area, turbine efficiency, evaporator pinch point, pump efficiency, and turbine input temperature. The ideal cost rate and exergy efficiency were 1.299 USD/h and 19.100%, respectively. The feasibility of the suggested module was studied in coastal areas of San Francisco (America), Dubai (Asia), Barcelona (Europe), and Melbourne (Oceania). Finally, the system's effectiveness in providing the power, cooling, and heating demands of a two-floor two-unit building during the year was evaluated.

MultiOptForest: An interactive multi-objective optimization tool for forest planning and scenario analysis

MultiOptForest is an open-source software designed to simplify building and solving multi-objective optimization problems for forest planning. It aims to find the optimal portfolio of management regimes that balance the objectives regarding multiple forest ecosystem services and biodiversity. The software flexibly imports data, allowing for the use of a variety of forest simulator outputs. The user provides preference information through a user-friendly graphical interface, where the range of possible values for each objective is provided. MultiOptForest solves the optimization problem producing a set of Pareto optimal solutions, i.e., solutions where none of the objectives can be improved without compromising others. MultiOptForest is versatile enough to design a Pareto optimal forest plan for a small holding to assess management and the trade-off between multiple policy objectives impacting the development of forests across regions and countries.

Optimization of EDM process parameters using MCDM based on customer preference rating method

Nowadays, multi-objective optimization tools have been used in manufacturing processes to decide the optimal process parameters. Optimization method with a more advanced tool is required to obtain the finest EDM process parameters such as peak current, voltage, pulse on duration, pulse off duration, pressure. This research work utilizes the Multi-Criteria Decision Making Method (MCDM) for identifying the optimal process parameters according to customer preference rating method (CPR). CPR draws a weightage to the output responses of the machine and characteristics of the machined surface as per customer requirement. Genetic Algorithm (GA) and TOPSIS are extensively involved in solving multi-objective optimization problems. Optimization of process parameters were determined using Genetic Algorithm (GA) integrated with TOPSIS to achieve higher Metal Removal Rate, Micro Hardness, low surface roughness, tool wear rate and actual depth. There are fifty Pareto-optimal solutions performed and the best solution has been selected based on rank. The optimal solution of the GA was given to the TOPSIS as input for further improvement. The optimal value obtained through GA has been enhanced with TOPSIS.

Integrated SUSTAIN-SWMM-MCDM Approach for Optimal Selection of LID Practices in Urban Stormwater Systems

Rapid urbanization has increased impervious areas, leading to a higher flood hazard across cities worldwide. Low Impact Development (LID) practices have shown efficacy in reducing urban runoff; nevertheless, choosing the best combinations in terms of implementation cost and performance is of great importance. The present study introduces a framework based on green infrastructure, multi-objective optimization, and decision support tools to determine the most cost-effective LID solutions. The Storm Water Management Model (SWMM) was employed for rainfall-runoff and hydraulic modeling in Region 1, District 11 of Tehran, Iran. Six scenarios of different combinations of LID practices were developed. The system for Urban Stormwater Treatment and Analysis Integration (SUSTAIN) was used to optimize and evaluate each scenario. The selected solutions were imported to the SWMM to evaluate the stormwater system performance. Then, two multi criteria decision making (MCDM) models, including TOPSIS and COPRAS, were employed to rank the scenarios based on four technical and economic criteria. Results showed that scenario 4, consisting of rain barrels, porous pavements, and vegetated swales, had the best performance under TOPSIS with a 7.68 million USD and reduced the runoff volume and peak flow by 20.77% and 19.2%, respectively. However, Under the COPRAS method, Scenario 2 with a combination of rain barrels, bio-retention cells, and vegetated swales showed higher performance than the other scenarios with 3.25 million USD and led to a 15% reduction in the runoff volume and 4.30% in the peak flow. The COPRAS method was more sensitive to cost weights and chose the most economical scenario as the ideal. However, Scenario 4 concluded to be more feasible due to spatial limitations in the study area. The proposed SWMM—SUSTAIN—MCDM framework could be helpful to decision-makers in the design, performance evaluation, cost estimation, and selection of optimal scenarios.

In Vivo Sampling of Intracellular Heterogeneity of Pseudomonas putida Enables Multiobjective Optimization of Genetic Devices.

The inner physicochemical heterogeneity of bacterial cells generates three-dimensional (3D)-dependent variations of resources for effective expression of given chromosomally located genes. This fact has been exploited for adjusting the most favorable parameters for implanting a complex device for optogenetic control of biofilm formation in the soil bacterium Pseudomonas putida. To this end, a DNA segment encoding a superactive variant of the Caulobacter crescendus diguanylate cyclase PleD expressed under the control of the cyanobacterial light-responsive CcaSR system was placed in a mini-Tn5 transposon vector and randomly inserted through the chromosome of wild-type and biofilm-deficient variants of P. putida lacking the wsp gene cluster. This operation delivered a collection of clones covering a whole range of biofilm-building capacities and dynamic ranges in response to green light. Since the phenotypic output of the device depends on a large number of parameters (multiple promoters, RNA stability, translational efficacy, metabolic precursors, protein folding, etc.), we argue that random chromosomal insertions enable sampling the intracellular milieu for an optimal set of resources that deliver a preset phenotypic specification. Results thus support the notion that the context dependency can be exploited as a tool for multiobjective optimization, rather than a foe to be suppressed in Synthetic Biology constructs.

Effect of reducing atmosphere on the direct reduction of iron oxides pellets

The present paper describes the direct reduction kinetics of industrial iron oxide pellets in various compositions of hydrogen and carbon monoxide. Different types of pellets with various percentages of total iron content and metal oxides were examined. They were reduced at different temperatures and pressure (700–1100 °C and 1–6 bar) in various atmosphere with different contents of hydrogen and carbon monoxide. The reduction behaviour was described in terms of time to reduction, rate of reduction and kinetics constant. All the obtained results were analysed through the employment of a commercial multi-objective optimization tool (modeFrontier) in order to precisely define the weight that each single parameter has on the reduction behaviour of the pellets. The performed analyses allowed also to correlate the different processing parameters and the pellets properties in order to define the kinetics conditions as well as the factors limiting the reduction process.

Critical analysis of variable atmosphere gaseous reduction of iron oxides pellets

ABSTRACT The paper deals with the analyses of the direct reduction kinetics of industrial iron oxide pellets. Various types of pellets were reduced at different temperatures and pressure (700–1100°C and 1–6 bar) in various atmospheres with different contents of hydrogen and carbon monoxide. The reduction behaviour was described in terms of time to reduction, rate of reduction, and kinetics constant. For those pellets reduced in presence of carbon monoxide, also the carbon percentage in the reduced pellets was taken into account. All the obtained results were analysed through the employment of a commercial multi-objective optimisation tool (modeFrontier) in order to precisely define the weight that each single parameter has on the reduction behaviour of the pellets. The performed analyses allowed also to correlate the different processing parameters and the pellets properties in order to define the kinetics conditions as well as the factors limiting the reduction process.

Feasibility Analysis of a Fuel Cell-Based Tri-Generation Energy System Via the Adoption of a Multi-Objective Optimization Tool

Abstract The present paper investigates the feasibility of a tri-generation energy system in an industrial scenario with a modest size in terms of levels of electricity, heat, and cooling consumption. The technology under consideration is the fuel cell technology, both solid oxide fuel cells and proton exchange membrane fuel cells (PEMFCs), compared to other more mature technologies, such as micro gas turbines. The proposed investigation takes into account several scenarios: the existing economy and state-of-the-art technical key performance indicators of the involved energy systems; the state-of-the-art technical key performance indicators of the involved technologies and economic subsidies; and a future scenario that takes into account economies of scale and better performance using the key metrics for fuel cell technology forecasted as 2030 target at European level. The PEMFCs with lithium-ion storage showed total efficiencies in the order of 75% over three reference periods. In terms of emissions, they guarantee a decrease in carbon dioxide equivalent released into the atmosphere equal to 40% of the reference emissions for a separate generation.

Hydrogen‐Based Direct Reduction of Iron Oxides Pellets Modeling

The present study deals with the analyses of the direct reduction kinetics during the hydrogen reduction of industrial iron oxide pellets. Various types of pellets with different percentage of total iron content and metal oxides are examined. They are reduced at different temperatures and pressure (700–1100 °C and 1–6 bar) in hydrogen atmosphere. The reduction behavior is described in terms of time to reduction, rate of reduction, and kinetics constant. All the obtained results are analyzed through the employment of a commercial multiobjective optimization tool to precisely define the weight that each single parameter has on the reduction behavior. It is shown that from the point of view of the processing conditions, temperature is the main factor influencing the time to total reduction. From the point of view of the pellets properties, it is mainly influenced by the total iron percentage and then by porosity and basicity index. Also, the kinetics behavior is largely influenced by the reduction temperature even if it is mainly governed by the porosity and pores size from the point of view of the reduced pellets. The reduction rate is also mainly influenced by temperature and then by iron percentage, gas pressure, basicity index, and porosity.

MULTIOBJECTIVE OPTIMIZATION OF THE INTERGENERATIONAL RESIDENTIAL SPACE WITH THE GOAL OF DAYLIGHTING AND THERMAL COMFORT

ABSTRACT (1) Background: An ageing population and two-child policy have led to the transformation of China’s family structure, and multigenerational residences account for an increasing proportion of mainstream family residences. Different generations of residents have great differences in behaviour patterns and health requirements, but existing residential buildings are not especially designed for health needs. (2) Methods: First, based on relevant codes and a questionnaire survey, the spatial needs and behaviour patterns of different generations of residents are obtained, and the benchmark model is established based on the Grasshopper (GH) parametric platform. Then, based on the GH platform and the building simulation plug-in, which are Ladybug Tools and a multiobjective optimization tool named Wallacei, daylight autonomy (DA), useful daylight illuminance (UDI), and the proportion of thermal discomfort hours (PDH) are taken as daylighting and thermal comfort indicators, and the bedroom location, orientation, bay size, depth to bay ratio, window form, window-to-wall ratio, and horizontal shading width are optimized. Finally, the Pareto front is analysed by the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) comprehensive evaluation method, and the optimal solution is compared with existing multigenerational residences in Tianjin. (3) Results: First, the middle room, oriented south or south by east, large bay, small window-to-wall ratio, and bay window can effectively be improved for indoor thermal comfort, while rooms that are oriented south or south by east, small depth bay ratio, large window-to-wall ratio and balcony can effectively be improved for daylighting quality. Second, compared with existing buildings in Tianjin, the DA, UDI, and PDH of the adult bedroom with the ideal solution are increased by 33.7%, 3.5%, and 10.8%, respectively; the DA, UDI, and PDH of the child bedroom with the ideal solution are increased by 15.5%, 4.2%, and 4.9%, respectively; and the DA, UDI, and PDH of the elderly individual bedroom with the ideal solution are increased by 42.7%, 4.9%, and 1.7%, respectively. (4) Conclusions: The optimization scheme is substantially improved for the health of the indoor daylight and thermal environment of existing housing, and it provides a scientific and quantitative decision-making basis for the healthy design of multigenerational residences.

MOO-GAPS: A multi-objective optimization model for global animal production and sustainability

Agricultural and livestock production involves significant trade-offs between multiple sustainable development goals, including reducing hunger and poverty, and reducing emissions of greenhouse gasses. Here we describe a multi-objective optimization tool for livestock production to evaluate trade-offs among environmental and economic objectives with high spatial granularity and the capability to be aggregated to national or global scales. We illustrate the use of this tool analysing how and where to produce beef to optimize the weighted sum of environmental and economic objectives, in this case minimizing greenhouse gas emissions and minimizing costs of production. We present how the outputs of this model can inform a sustainable transition of the industry and policy decisions, with a focus on inherent trade-offs. By comparing optimal production with current production, we highlight the potential environmental and economic efficiencies that can be made by changing the location or methods of production. As such, this tool provides a platform to identify trade-offs and synergies among multiple diverse sustainability goals, critical in the livestock and agricultural sectors.

An improved marine predator algorithm based on epsilon dominance and Pareto archive for multi-objective optimization

Solving multi-objective optimization problems plays an important role in several applications. Recently, the Marine Predator Algorithm (MPA) was introduced for solving single objective optimization problems inspired by the behaviour of marine predator in their search for a prey. This paper proposes a modified MPA based framework called Guided Multi-objective Marine Predator Algorithm (GMOMPA) to solve multi-objective optimization problems. The proposed GMOMPA incorporates an external archive to help store the optimal Pareto set solution and guide the particles during the exploitation of the search space. For obtaining non-dominated solutions, the Pareto dominance concept is utilized while the epsilon dominance is considered to update the archive’s sorted solutions. In this context, the epsilon dominance concept extends the diversity and exploration of the solutions. Further, a fast non-dominated solution and crowding distance are introduced to update the particle’s position, while maintaining the diversity and ensuring a fast convergence towards the Pareto optimal. The proposed GMOMPA is evaluated on several different benchmark test functions including multi-objective ZDT, DTLZ, UF, and WFG test functions as well as the recent multi-objective multimodal CEC 2020 test functions. Moreover, the performance of the proposed GMOMPA is compared with well-known multi-objective optimization algorithms. The obtained results show that the proposed GMOMPA is a good tool for multi-objective optimization and has significant advantages over several state-of-the-art algorithms in almost all of the test functions.