Multi-criteria Optimization Research Articles

Eco-friendly wastewater treatment using a crab shell-based liquid bio-coagulant: Multi-criteria decision analysis related to different pollutants separation

The protein-rich liquid resulting from the deproteinization step of the chitin/chitosan production process is proposed as a novel wastewater bio-coagulant. The raw material for producing the bio-coagulant is the crab shell contained in discarded fishery by-products. The protein content of the liquid, determined by OPA assay, was 31.6 mg/L. FTIR spectra proved the presence of chitin, and SEM images showed the presence of a nanoparticulated chito-protein complex in the liquid. Coagulation tests were performed with fisheries wastewater, in which the influence of different parameters, such as the bio-coagulant dose, the pH, the agitation speed, and the settling time, on the effectiveness of the bio-coagulant were investigated. Since different parameters with values in wide ranges may affect the fisheries wastewater treatment by the use of the new liquid bio-coagulant, a multicriterial optimization analysis based on Box-Behnken and response surface method (RSM) was applied. The optimal removal degrees, in terms of turbidity (98.91%), BOD5 (92.05%), and COD (78.92%) were achieved at a bio-coagulant dosage of 17.5 mL/L of fish processing wastewater (FPW) at pH 11.3 and temperature of 25°C. A good fit resulted from the experimental data, the Box-Behnken regression models, and response surface-calculated data. The results revealed new bio-coagulant effectiveness similar or superior to the commonly used chemical coagulants (ferric chloride, aluminum sulfate) and even to commercial chitosan. The obtained data indicate that the new natural liquid bio-coagulant is environmentally friendly and suitable for fisheries wastewater treatment.

A novel cost-palatability bi-objective approach to the menu planning problem with an innovative similarity metric using a path relinking algorithm

Bad eating habits are considered with low exercise, tobacco, and alcohol use, among the greatest health risk factors in high-income countries. Here, the spread of non-communicable diseases related to diet contrasts with the increasing availability of diet guidelines and health information. We are all left wondering, why are we not eating healthier? Palatability and cost appear as the two most important barriers to adherence to any prescribed diet. This work aims to generate high-adherence healthy menus attending to Mediterranean standards, modelling and solving a cost palatability bi-objective optimisation problem. Although the latter is implicitly considered in most current models, its use as a quantifiable objective is novel. For this, a novel Similarity Function is introduced, which evaluates the proximity of two different menus and returns a similarity metric between 0 and 1. A multi-criteria optimisation method is used to design nutritious menu plans and analyse the trade-off between the cost and palatability of menus. A Path Relinking algorithm is later applied to improve the objective values, with specific considerations to palatability, designing healthy menus that can beat those adherence barriers.

A hybrid solar-driven vacuum thermionic generator and looped multi-stage thermoacoustically driven cryocooler system: Exergy- and emergy-based analysis and optimization

Significant high-quality heat is wasted in the vacuum thermionic generator (VTIG), which can be efficiently utilized as a prime mover of a bottoming system for cogeneration applications. For this purpose, a new environmental-friendly hybrid system composed of a heliostat solar field, VTIG, and looped multi-stage thermoacoustically driven cryocooler (LMTC) is established, in which the high-temperature heat source of the solar receiver runs the VTIG to generate power, and the LMTC recovers the waste heat of the VTIG to produce a cooling load. Thermodynamic, economic, and environmental analyses of the system are carried out based on exergy and emergy concepts. Moreover, a parametric study is performed to assess the effect of design parameters on the system's thermodynamic, economic, and environmental criteria. Finally, the multi-criteria salp swarm optimization algorithm and decision-making procedures are conducted to improve the exergetic performance and decrease the system's cost and monetary emergy rates along with the environmental impact and ecological emergy rate. Findings depict that at the reliable, optimal operation of the system, the exergetic efficiency can reach 29.36% with a maximum power of 17.2 MW and cooling load of 0.260 MW. The system's cost and monetary emergy rate can be reduced to 0.059 $/s and 5.94 × 1010 seJ/s, with 10.6% and 10% reductions, respectively. Moreover, the environmental impact and ecological emergy rates decline by 6% and 7.4%, respectively. The theoretical findings may offer guidance for the optimum designing and practical running of such a solar solid-state cogeneration system.

Multi-attribute decision making based on VIKOR with probabilistic linguistic term sets: An application to the risk evaluation of foreign direct investment.

The multiple global environments have triggered changes in the international environment, leading to a sharp decline of foreign direct investment (FDI) compared to pre-pandemic level. To evaluate the investment risk of FDI and make optimal investment decision becomes the most important issue for investors. This paper focuses on the evaluation of investment risk for FDI. First, an index system for risk evaluation of FDI is constructed. Then, we introduce the probabilistic linguistic entropy and cross entropy measures, based on which, a programming model is developed to identify the objective attribute weights. A composite weight derivation method, which takes both the objective attribute weights and the subjective attribute weights into account, is further introduced. In view of attributes' uncertainty and fuzziness and the conflicting characteristics of some attributes, the VIKOR (the Serbian name: VlseKriterijumska Optimizacija I Kompromisno Resenje, means multi-criteria optimization and compromise solution) method is used to evaluate the risk of FDI under the probabilistic linguistic environment. Furthermore, a case study is presented to illustrate the proposed method. The comparative analysis and some further discussions verify the validity of the proposed method for the FDI risk evaluation.

SISS-MCO: large scale sparsity-induced spot selection for fast and fully-automated robust multi-criteria optimisation of proton plans

Objective. Intensity modulated proton therapy (IMPT) is an emerging treatment modality for cancer. However, treatment planning for IMPT is labour-intensive and time-consuming. We have developed a novel approach for multi-criteria optimisation (MCO) of robust IMPT plans (SISS-MCO) that is fully automated and fast, and we compare it for head and neck, cervix, and prostate tumours to a previously published method for automated robust MCO (IPBR-MCO, van de Water 2013). Approach. In both auto-planning approaches, the applied automated MCO of spot weights was performed with wish-list driven prioritised optimisation (Breedveld 2012). In SISS-MCO, spot weight MCO was applied once for every patient after sparsity-induced spot selection (SISS) for pre-selection of the most relevant spots from a large input set of candidate spots. IPBR-MCO had several iterations of spot re-sampling, each followed by MCO of the weights of the current spots. Main results. Compared to the published IPBR-MCO, the novel SISS-MCO resulted in similar or slightly superior plan quality. Optimisation times were reduced by a factor of 6 i.e. from 287 to 47 min. Numbers of spots and energy layers in the final plans were similar. Significance. The novel SISS-MCO automatically generated high-quality robust IMPT plans. Compared to a published algorithm for automated robust IMPT planning, optimisation times were reduced on average by a factor of 6. Moreover, SISS-MCO is a large scale approach; this enables optimisation of more complex wish-lists, and novel research opportunities in proton therapy.

Combining discrete and continuous information for multi-criteria optimization problems

In multi-criteria optimization problems that originate from real-world decision making tasks, we often find the following structure: There is an underlying continuous, possibly even convex model for the multiple outcome measures depending on the design variables, but these outcomes are additionally assigned to discrete categories according to their desirability for the decision maker. Multi-criteria deliberations may then take place at the level of these discrete labels, while the calculation of a specific design remains a continuous problem. In this work, we analyze this type of problem and provide theoretical results about its solution set. We prove that the discrete decision problem can be tackled by solving scalarizations of the underlying continuous model. Based on our analysis we propose multiple algorithmic approaches that are specifically suited to handle these problems. We compare the algorithms based on a set of test problems. Furthermore, we apply our methods to a real-world radiotherapy planning example.

Comparative Study of Eclipse and RayStation Multi-Criteria Optimization-Based Prostate Radiotherapy Treatment Planning Quality.

Multi-criteria optimization (MCO) function has been available on commercial radiotherapy (RT) treatment planning systems to improve plan quality; however, no study has compared Eclipse and RayStation MCO functions for prostate RT planning. The purpose of this study was to compare prostate RT MCO plan qualities in terms of discrepancies between Pareto optimal and final deliverable plans, and dosimetric impact of final deliverable plans. In total, 25 computed tomography datasets of prostate cancer patients were used for Eclipse (version 16.1) and RayStation (version 12A) MCO-based plannings with doses received by 98% of planning target volume having 76 Gy prescription (PTV76D98%) and 50% of rectum (rectum D50%) selected as trade-off criteria. Pareto optimal and final deliverable plan discrepancies were determined based on PTV76D98% and rectum D50% percentage differences. Their final deliverable plans were compared in terms of doses received by PTV76 and other structures including rectum, and PTV76 homogeneity index (HI) and conformity index (CI), using a t-test. Both systems showed discrepancies between Pareto optimal and final deliverable plans (Eclipse: -0.89% (PTV76D98%) and -2.49% (Rectum D50%); RayStation: 3.56% (PTV76D98%) and -1.96% (Rectum D50%)). Statistically significantly different average values of PTV76D98%,HI and CI, and mean dose received by rectum (Eclipse: 76.07 Gy, 0.06, 1.05 and 39.36 Gy; RayStation: 70.43 Gy, 0.11, 0.87 and 51.65 Gy) are noted, respectively (p < 0.001). Eclipse MCO-based prostate RT plan quality appears better than that of RayStation.

Environmental assessment and CO2emissions of Brayton cycle configurations based on exergo-sustainability, economic and ecological efficiency using multi-criteria optimization technique

The performance indicators of Brayton cycle configurations (BCY) for the topping cycle application are presented. The indicators include exergy efficiency, ecological efficiency, sustainability index, environmental impact, and economic parameters. The study's objective is to access the key indicators of sustainability and investment cost to provide valid information for the choice of GT configuration as topping cycles. Five BCY configurations (Model 1 to Model 5) were studied. The maximum exergy efficiency of 28 % was obtained across the studied models. In addition, the waste exergy ratios, environmental effect factors, and CO2 emissions were determined for each model. The CO2 emissions were found to vary from 102.8 to 168 kg/MWh. Model 1 and Model 5 had the highest payback periods of 2.3 and 3.6 years, respectively, with the least unit cost of energy. Similarly, the highest cost of investment was obtained with Model 5. Results from the TOPPIS analysis show that the closeness to the final positive ideal solution varied from 0.218 to 0. 56 across the BCYs. The best model close to ideality was model 5 and thus ranked first and based principally on economic, technical, and environmental sustainability. Furthermore, the optimization results show that there are prospects for system retrofitting.

Development of optimization mathematical models for making compromise decisions on the efficiency of an agricultural moving power units in fleet

In order to build a calculation routine for making compromise decisions on the efficiency of a fleet of agricultural moving power units, this work analyzed mathematical models characterizing the efficiency of the agricultural moving power units fleet in the context of digitalization of agriculture. In order to select initial data for solving a multicriteria optimization problem for making compromise decisions, the main quality criteria for the efficiency of an agricultural moving power units fleet and their functional limitations were selected. A formulation of the problem of multi-criteria optimization of the efficiency of the agricultural moving power units in fleet and a list of variable parameters, in the amount of 14, as well as the range of their variation have been generated for further adjustment of the problem statement for making compromise decisions when assessing the efficiency of the agricultural moving power units in fleet. In the future, when forming a mathematical and software complex, according to multi-criteria assessment, they will be significant for the decision maker (DM)

Directions for modifying the artificial bee colony algorithm to optimize control parameters for complex systems

In recent years, bioinspired algorithms based on the use of a population approach and a probabilistic search strategy have become especially popular among researchers involved in multidimensional and multicriteria optimization. Such algorithms are based on the principles of cooperative behavior of a decentralized self-organizing colony of living organisms (bees, ants, birds, etc.) to achieve certain goals (for example, to meet nutritional needs). However, their practical application encounters a number of difficulties leading to a decrease in convergence. This article discusses the possibility of modifying the artificial bee colony algorithm by using a hybridization strategy with various data mining methods. One of these difficulties is the lack of a reasonable approach to determining initial search positions. As a solution, it is proposed to divide the population into clusters, the centers of which will be the initial positions. The need for interaction between individuals makes it advisable to use fuzzy clustering, which allows the formation of intersecting clusters. Another difficulty is associated with the choice of “free” parameters, for which the authors have not developed recommendations for choosing their optimal values. To solve this problem, it is proposed to use the idea of coevolution, which consists in the parallel launch of several interacting subpopulations, for each of which different “settings” are applied. The proposed algorithm is applicable to multidimensional optimization tasks, in which it is necessary to find such a combination of different types of elements belonging to some “large” population that will ensure the achievement of the maximum effect under given restrictions. Examples of such tasks are determining the species and quantitative composition of plants to form the terrestrial ecosystem of a carbon farm or mass recruiting, which consists of selecting a large number of personnel for the same positions.

Thermo-economic analysis and multi-criteria optimization of an integrated biomass-to-energy power plant

A biomass-driven integrated system comprising anaerobic digestion, gasification, proton exchange membrane electrolyzer (PEME), Sabatier reactor, solid oxide fuel cell (SOFC), a gas turbine, steam turbine and organic Rankine cycle (ORC) was proposed in this study. Biomass feedstocks in form of animal waste served as input in the digester and converted to biogas, and crop residue was converted to syngas in the gasifier. Upgraded syngas and methane from the bio-conversion process was fed to a SOFC-GT topping cycle, with heat recovery bottoming cycles of steam turbine and organic Rankine cycles. The proposed system was assessed from energy, exergy and economic viewpoints in Engineering Equation Solver (EES) software. Parametric analysis was performed to ascertain the effect of design parameters on the plant's performance. Lastly, a multi-criteria optimization was performed using multi-objective genetic algorithm (MOGA) in MATLAB to maximize exergy efficiency and minimize levelized cost of electricity, as well as selection of best ORC working fluid from six preselected candidates (MM, MDM, cyclopentane, cyclohexane, R1233zd(E), and R600a). According to the results, at optimum point the plant can attain energy and exergy efficiencies of 54.81 % and 44.87 %, respectively. The total power output is 9.05 MW, with levelized cost of electricity of 111.8 $/MWh. Hydrogen of 0.0023 kg/s with PEME efficiency of 73.73 % was obtained and further used in upgrading the syngas from LHV of 4.20–37.78 MJ/kg.

A novel hybrid multi-criteria optimization of 3D printing process using grey relational analysis (GRA) coupled with principal component analysis (PCA)

Fused deposition modeling (FDM) is renowned as a prominent approach in the realm of 3D printing, where objects are built layer by layer using a heated nozzle to extrude melted materials. This research was conducted to identify the most effective FDM process variables to enhance tensile strength while simultaneously reducing surface roughness. Polylactic Acid (PLA) was chosen to fabricate test samples, showcasing the applications of 3D printing. In the course of this research, we conducted a series of 27 experiments to investigate the fundamental relationship between the parameters and the corresponding responses. The central aim of this study lies in optimizing the input variables viz. printing speed, layer thickness, and carbon deposition (C-deposition) for the technological manufacturing process of embossing parts in the context of Industry 4.0. To enhance both tensile strength and surface roughness simultaneously, a new hybrid method has been suggested. This approach integrates grey relational analysis (GRA) with principal component analysis (PCA) to determine the optimal combination of process parameters in the 3D printing process. Notably, the experiment trial exhibited the highest grey relational grade (GRG), indicating optimal process parameter settings at a printing speed of 100 mm s−1, layer thickness of 0.1 mm, and C-deposition of 15 mg respectively. Additionally, mathematical models are created through response surface methodology to explore the impact of FDM parameters on the grey relational grade. The findings from this study can be utilized in various industries and applications where FDM 3D printing is employed.

Development of patient and catheter specific error thresholds for high dose rate prostate brachytherapy.

In-vivo source tracking has been an active topic of research in the field of high-dose rate brachytherapy in recent years to verify accuracy in treatment delivery. Although detection systems for source tracking are being developed, the allowable threshold of treatment error is still unknown and is likely patient-specific due to anatomy and planning variation. The purpose of this study was to determine patient and catheter-specific shift error thresholds for in-vivo source tracking during high-dose-rate prostate brachytherapy (HDRPBT). A module was developed in the previously described graphical processor unit multi-criteria optimization (gMCO) algorithm. The module generates systematic catheter shift errors retrospectively into HDRPBT treatment plans, performed on 50 patients. The catheter shift model iterates through the number of catheters shifted in the plan (from 1 to all catheters), the direction of shift (superior, inferior, medial, lateral, cranial, and caudal), and the magnitude of catheter shift (1-6mm). For each combination of these parameters, 200 error plans were generated, randomly selecting the catheters in the plan to shift. After shifts were applied, dose volume histogram (DVH) parameters were re-calculated. Catheter shift thresholds were then derived based on plans where DVH parameters were clinically unacceptable (prostate V100<95%, urethra D0.1cc>118%, and rectum Dmax>80%). Catheter thresholds were also Pearson correlated to catheter robustness values. Patient-specific thresholds varied between 1 to 6mm for all organs, in all shift directions. Overall, patient-specific thresholds typically decrease with an increasing number of catheters shifted. Anterior and inferior directions were less sensitive than other directions. Pearson's correlation test showed a strong correlation between catheter robustness and catheter thresholds for the rectum and urethra, with correlation values of -0.81 and -0.74, respectively (p<0.01), but no correlation was found for the prostate. It was possible to determine thresholds for each patient, with thresholds showing dependence on shift direction, and number of catheters shifted. Not every catheter combination is explorable, however, this study shows the feasibility to determine patient-specific thresholds for clinical application. The correlation of patient-specific thresholds with the equivalent robustness value indicated the need for robustness consideration during plan optimization and treatment planning.

A comprehensive assessment of nanomaterials reinforced lightweight aggregate concrete containing high-volume artificial shale ceramsite

Poor mechanical behavior of lightweight aggregate concrete limits its application. To improve this situation, nano-silica (NS) and carbon nanotubes (CNTs) were used in this study. The impact of NS and CNTs on fresh and mechanical properties, general microstructure and interfacial transition zone (ITZ) of lightweight aggregate concrete containing high-volume artificial shale ceramsite (ASC-LAC) was evaluated. Furthermore, economic sustainability was conducted to provide a comprehensive evaluation on the overall performance of ASC-LAC. Results showed that the synergic effects of NS and CNTs were positive when the replacement of NS and CNTs was smaller than 2.5 wt% and 0.25 wt%, respectively. Incorporating NS caused a decrease in both slump and air content, while adding CNTs caused a decrease in slump but a slight increase in air content. ASC-LAC incorporating 1.0 wt% NS and 0.10 wt% CNTs was found to have the best mechanical performance given a rise of 47.7 % and 17.0 % in compressive and flexural strength, respectively. However, given a higher NS and CNTs replacement (i.e., 2.5 wt% and 0.25 wt%, respectively), a worsened ITZ with less C-S-H gel was identified, which was responsible for the decreased concrete performance. Multi-criteria optimization results showed that ASC-LAC at a mono doped of 1.0 wt% NS or 0.10 wt% CNTs or combined 1.0 wt% NS and 0.10 wt% CNTs had better overall performance considering five-dimensional indices, i.e., slump, air content, compressive strength, flexural strength and cost efficiency.

Information technology of synthesis of liquid dosing process control

Problem. Currently, different solutions are available on the global market for liquid dosing. There is no universal technology, as the same substances can be dosed using different types of equipment. Therefore, manufacturers face the complex task of determining the most optimal kind of equipment to achieve their goals. These issues are a complex multi-criteria scientific problem, and to effectively address them, it is necessary to develop a technology for synthesizing a liquid dosing control system using multi-criteria optimization methods in different conditions of information determinacy. Goal. The paper aims to increase the efficiency of liquid dosing control by developing information technology for the control system synthesizing. The object of the paper is the process of information technology development for synthesizing the liquid dosing control system. The subject of the paper is the models for the selection of hardware and software for the liquid dosing control system. Methodology. The study analyses and provides a classification of liquid dosing technology and analyses existing liquid dosing control systems. A structural model of information technology for synthesising a liquid dosing control system is developed, and criteria for selecting the elements of the liquid dosing control system are justified. A model for selecting PLC and SCADA systems for synthesising the liquid dosing control system is also developed using hierarchical analysis methods and multi-criteria evaluation and optimisation. Results. The paper resulted in the following: analysis and classification of liquid dosing technology; analysis of existing liquid dosing control systems; development of a structural model of the information technology of synthesising a liquid dosing control system; justification of criteria for selecting the elements of the liquid dosing control system, and the development of a model for selecting PLC and SCADA systems for the synthesis of the liquid dosing control system using hierarchical analysis methods and multi-criteria evaluation and optimisation. Originality. Further development of the methods of hierarchy analysis and multi-criteria assessment and optimization has been achieved by applying them to a specific field – the development of models for selecting hardware and software for the liquid dosing process control system. Practical value. The proposed information technology of synthesising a liquid dosing control system, as well as the mathematical models for selecting hardware and software for the liquid dosing process control system, will reduce the time and cost required to develop such a system.

Validation of Fully Automated Robust Multicriterial Treatment Planning for Head and Neck Cancer IMPT

Our purpose was to compare robust intensity modulated proton therapy (IMPT) plans, automatically generated with wish-list-based multicriterial optimization as implemented in Erasmus-iCycle, with manually created robust clinical IMPT plans for patients with head and neck cancer. Thirty-three patients with head and neck cancer were retrospectively included. All patients were previously treated with a manually created IMPT plan with 7000 cGy dose prescription to the primary tumor (clinical target volume [CTV]7000) and 5425 cGy dose prescription to the bilateral elective volumes (CTV5425). Plans had a 4-beam field configuration and were generated with scenario-based robust optimization (21 scenarios, 3-mm setup error, and ±3% density uncertainty for the CTVs). Three clinical plans were used to configure the Erasmus-iCycle wish-list for automated generation of robust IMPT plans for the other 30 included patients, in line with clinical planning requirements. Automatically and manually generated IMPT plans were compared for (robust) target coverage, organ-at-risk (OAR) doses, and normal tissue complication probabilities (NTCP). No manual fine-tuning of automatically generated plans was performed. For all automatically generated plans, voxel-wise minimum D98% values for the CTVs were within clinical constraints and similar to manual plans. All investigated OAR parameters were favorable in the automatically generated plans (all P < .001). Median reductions in mean dose to OARs went up to 667 cGy for the inferior pharyngeal constrictor muscle, and median reductions in D0.03cm3 in serial OARs ranged up to 1795 cGy for the spinal cord surface. The observed lower mean dose in parallel OARs resulted in statistically significant lower NTCP for xerostomia (grade ≥2: 34.4% vs 38.0%; grade ≥3: 9.0% vs 10.2%) and dysphagia (grade ≥2: 11.8% vs 15.0%; grade ≥3: 1.8% vs 2.8%). Erasmus-iCycle was able to produce IMPT dose distributions fully automatically with similar (robust) target coverage and improved OAR doses and NTCPs compared with clinical manual planning, with negligible hands-on planning workload.

Multi-Criteria Optimization Conditions for the Recovery of Bioactive Compounds from Levisticum officinale WDJ Koch Roots Using Green and Sustainable Ultrasound-Assisted Extraction

Given that ultrasound-assisted aqueous extraction is gaining importance within “green technology” and to increase the efficiency of extracting bioactive compounds from Levisticum officinale root waste, optimization of its parameters was undertaken. Multi-objective (multi-criteria) optimization can be an extremely promising tool not only for designing and analyzing the extraction process, but also for making process-control decisions. Therefore, the main objective of this study was to develop and optimize an environmentally friendly ultrasound-assisted extraction methodology for the aqueous extraction of bioactive compounds from the roots of Levisticum officinale, which are considered a by-product. The focus was on determining the optimal extraction conditions of the independent variables, such as solid–liquid ratio, extraction time and ultrasound power, so that the optimized extracts present the highest bioactive potential expressed in terms of levels of phenolic compounds, flavonoids, sugars and antioxidant potential. Based on the Pareto-optimal solution sets, it was found that to maximize the criteria, aqueous extraction should be carried out at a Levisticum officinale biomass/solvent ratio of 0.0643 g/mL for a time of 8.1429 to 9.0000 min, with ultrasound assistance of 162.8571 to 201.4286 W. Among the compromise solutions, the so-called “best efficient solution” was indicated as the solution for which the Euclidean distance from the ideal point of Utopia was the smallest (among all analyzed points of the collection), which had coordinates x1comp = 0.0750 g/mL, x2comp = 9.0000 min and x3comp = 214.2857 W. The results obtained will provide a valuable tool to assist in the decision-making process of controlling such an extraction process.

Human-robot collaborative task planning for assembly system productivity enhancement

PurposeIn the current era of Industry 4.0, the manufacturing industries are striving toward mass production with mass customization by considering human–robot collaboration. This study aims to propose the reconfiguration of assembly systems by incorporating multiple humans with robots using a human–robot task allocation (HRTA) to enhance productivity.Design/methodology/approachA human–robot task scheduling approach has been developed by considering task suitability, resource availability and resource selection through multicriteria optimization using the Linear Regression with Optimal Point and Minimum Distance Calculation algorithm. Using line-balancing techniques, the approach estimates the optimum number of resources required for assembly tasks operating by minimum idle time.FindingsThe task allocation schedule for a case study involving a punching press was solved using human–robot collaboration, and the approach incorporated the optimum number of appropriate resources to handle different types of proportion of resources.Originality/valueThis proposed work integrates the task allocation by human–robot collaboration and decrease the idle time of resource by integrating optimum number of resources.

An innovative process design and multi-criteria study/optimization of a biomass digestion-supercritical carbon dioxide scenario toward boosting a geothermal-driven cogeneration system for power and heat

In the pursuit of enhancing both sustainability and energy density in low-temperature, renewable energy-based cycles, the integration of high-temperature renewable streams is considered a key objective in multigenerational scenarios that focus on renewable energy. This integration is recognized for its ability to reduce irreversibility and facilitate the development of eco-friendly designs. Consequently, the development, analysis, and optimization of an innovative multigenerational system, which utilizes a combination of biomass feedstock and geothermal energy resources, are the aims of this study. In this system, the performance of a geothermal-driven subsystem is significantly enhanced by a biomass-fueled subsystem, contributing to a more efficient overall system. This enhancement involves the integration of biomass digestion with a supercritical CO2 process. The energetic flue gas generated in this process is then utilized to enhance the enthalpy level of geothermal water through a dual-flash process. This process includes an advanced Kalina cycle, enabling combined cooling, heating, and power generation. The feasibility of this structure is examined through a comprehensive analysis that encompasses thermodynamic and economic considerations. The performance optimization is targeted using the Multi-Objective Grey Wolf Optimization technique, and within this framework, two multi-criteria optimization scenarios are defined based on power and heat output, exergy efficiency, and the system's profitability. Furthermore, a detailed sensitivity analysis is conducted, where the impact of variations in five key decision parameters is evaluated. It is indicated by the results that 500.8 kW of power, 900.2 kW of heating, and 4.931 kW of cooling can be provided by the system, which also achieves an exergetic efficiency of 23.08 % and a payback period of 6.87 years.

Investigation of process parameters influence in waterjet peening of AZ31B-Mg alloy for bioimplant applications

The main objective of the study is to investigate the effects of parameters standoff-distance, waterjet pressure, traverse speed and number of passes used in the waterjet peening process on AZ31B-Mg alloy to assess surface grain refinement's potential and to improve the alloy's functionality in the biological environment, followed by the study of enhancement in hardening and roughening effect on AZ31B-Mg alloy surface. The optimal parametric condition required for investigating surface texture, X-ray diffraction analysis and electrochemical corrosion analysis with simulated body fluid was obtained through the multi-criteria optimisation, Technique for Order of Preference by Similarity to the Ideal Solution, which employs microhardness and surface roughness as response variables. The obtained results show significant improvement in mechanical and corrosion-inhibiting features of AZ31B-Mg alloy. Further, the biocompatibility of waterjet peened and unpeened surfaces was examined using 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-tetrazolium bromide assay and fluorescence analysis with MG63 cell lines.