Optimal Value Research Articles

Influence of Heating Elements Layout on Temperature Uniformity in a Large Size Heat Treatment Furnace

Computational Fluid Dynamics simulations were used to optimize heating elements placement on the walls of a large size electrical heat treatment furnace in order to reduce temperature gradients within the furnace and uneven temperatures on different faces of the blocks which could result in variabilities in final mechanical properties. Initial evaluations of different layouts highlighted the effectiveness of equipping two side walls. Subsequently, an optimal percentage of side wall coverage was identified through simulations and multi-objective evolutionary optimization. Genetic and pareto search algorithms yielded 10% and 14.2% as optimum values, respectively. Implementing an optimal coverage demonstrated significant reductions in surface temperature non-uniformity, surface-to-center temperature differential, and temperature differences between loaded blocks. These improvements suggest a potential for enhanced uniformity in mechanical properties across a batch of products. This approach presents a promising strategy for obtaining consistent and efficient heat treatment cycles in industrial heat treatment furnaces resulting in energy saving and improved product quality.

Industrial park wastewater treatment using a combined sono-pulsed electrochemical-coagulation process

Maintaining sustainable water management practices, preserving ecosystems, and safeguarding public health has depend on the treatment of wastewater. It has a significant impact on reducing water pollution and protecting the water supplies. Sono-pulsed electrochemical oxidation (S-PEO) process is a wastewater treatment method that uses ultrasound (US) waves and electrochemistry to enhance the pollutant removal efficiency. This study aim is to investigate the mineralization of industrial park wastewater by using pulsed electrochemical oxidation (PEO) and a combination of sonolysis and pulsed electrochemical oxidation (S-PEO) processes. The research focuses on the percentage elimination of chemical oxygen demand (COD) and color, as well as the assessment of power consumption. The study considers the influence of experimental parameters like initial pH, electrolysis time, and current to find the optimum conditions for maximum percentage removal efficiency with minimization of power consumption. According to the study, the optimum values of responses for the S-PEO process were obtained at pH=7, electrolysis time=40 min, and current =0.5 Amp. The optimum values for the maximum removal percentages of the responses COD and color were 97.04 %, and 99.70 %, respectively. The minimum of power consumption for S-PEO was 0.19 kWh/m3. These results demonstrate that, the superior efficiency of the S-PEO process and its feasibility in the removal of industrial wastewater pollutants. Optimizing parameters like pH, time, and current intensity is crucial for optimal performance, reducing energy consumption and operational costs. The S-PEO technique eliminates pollutants from wastewater efficiently and effectively, making it the suitable process when compared to the others.

Unified Modeling and Multi-Objective Optimization for Disassembly Line Balancing with Distinct Station Configurations

Disassembly line balancing (DLB) is a crucial optimization item in the recycling and remanufacturing of waste products. Considering the variations in the number of operators assigned to each station, this study investigates DLBs with six distinct station configurations: single-manned, multi-manned, single-robotic, multi-robotic, single-manned–robotic, and multi-manned–robotic setups. First, a unified mixed-integer programming (MIP) model is established for Type-I DLBs with each configuration to minimize four objectives: the number of stations, the number of operators, the total disassembly time, and the idle balancing index. To obtain more solutions, a novel bi-metric is proposed to replace the quadratic idle balancing index and is used in lexicographic optimization. Subsequently, based on the unified Type-I models, a unified MIP model for Type-II DLBs is established to minimize the cycle time, the number of operators, the total disassembly time, and the idle balancing index. Finally, the correctness of the established unified models and the effectiveness of the proposed bi-metric are verified by solving two disassembly cases of lighters and hairdryers, which further shows that the mathematical integration method of unified modeling has significant theoretical value for the multi-objective optimization of the DLBs with six distinct station configurations.

Sustainable EDM production of micro-textured die-surfaces: Modelling and optimizing the process using machine learning techniques

The present research explores the role of die-sinking EDM parameters, such as peak current, pulse duration, and gap voltage, and their proper selection for the cost-effective fabrication of negative micro-pillar-textured surfaces using a die-material of H13 steel alloy. The developed artificial neural networks (ANN) models of surface roughness and overcut outperform the response surface methodology models in predicting responses as higher ‘R2 values’ and lower ‘mean squared error’ with ANN models. A lower peak current of 2 A, lower pulse duration of 55.46 µs, and intermediate gap voltage of 37.83 V is selected from metaheuristic optimization approaches such as teaching–learning-based optimization and particle swarm optimization that are efficient and comparable with the desirability function-based approach while finding optimum parameter values. Further, sustainable fabrication of micro-textured H13 die surfaces is carried out on large areas using selected optimized parameters with a form tool electrode. The negative micro-pillar pattern surface demonstrates an average overcut of ∼ 40 ± 15 µm in comparison to the dimensions of the form tool. This research emphasized the sustainability of the EDM process by utilizing reusable dielectric fluid, prioritizing optimal parameters for high-quality fabrication using low electrical-energy utilization, and enabling large-scale production using a form tool.

Determination of cutting frequency for optimum herbage yield and nutritive value of Desho grass (Pennisetum glaucifolium) in western Oromia, Ethiopia

The experiment aimed to determine the optimal cutting frequency for desho grass to maximize dry matter yield and forage quality. Three cutting intervals (30, 45, and 60 days) were tested in a randomized complete block design with four replications. The agronomic parameters, dry matter yield, and chemical composition of the grass were collected and analyzed via standard procedures. The cutting frequency significantly affected the agronomic parameters and chemical composition but did not affect the DM yield. The tallest plants and most leaves were observed at 60-day intervals, while the tiller density was greater at 30 days. A relatively high DM yield (8.64 t/ha) was obtained by cutting desho grass at 30-day intervals, which was comparable to the results of the other treatments. The crude protein content decreased with increasing intervals, whereas the ADF content increased. Overall, cutting desho grass at 30–45 days optimized both the DM yield and nutritional value in the study areas.

A fuzzy multi-objective model for aggregate production planning considering energy consumption, carbon emission, chemical, solid waste, and wastewater

With a growing focus on environmental conservation, enterprises continuously reevaluate their conventional profit-driven business models. This shift has led to the development of green production planning models, attracting considerable attention in recent years. Previously, energy consumption or carbon emission were the most considered green parameters in the production planning problems. Nevertheless, several other essential green aspects such as reductions in chemical, solid waste, and wastewater are usually neglected. Therefore, this study proposes a multi-objective model for environmentally conscious aggregate production planning, simultaneously considering the above-mentioned green objectives. Additionally, the uncertain parameters of the model, including the green parameters, were modelled through fuzzy programming. Furthermore, this study incorporated a contradicting relationship between energy consumption cost and associated carbon emission, which was previously presented as non-contradicting. This relationship was illustrated by utilizing the time-of-use electricity tariff scheme. To solve the model, the initial step involved the defuzzification of uncertain attributes, followed by the transformation of the multi-objective model into a single-objective model. Finally, the IBM ILOG CPLEX Optimization Studio solver was utilized to solve the model based on the data collected from a precision engineering company. The generated production plan achieved an overall satisfaction level of 71%, which is reasonably acceptable to the decision maker. Moreover, sensitivity analysis has been conducted for the degree of optimism of the objective functions and the feasibility degree of the constraints. A general overview of the results associated with the performed sensitivity analysis indicated that the optimum values of the objective functions deteriorate as the parameters increase, though all remain within an acceptable range. The proposed study will assist decision makers in generating production plans that are both economically and environmentally feasible.

Moment-Based Reinforcement Learning for Ensemble Control.

Problems involving controlling the collective behavior of a population of structurally similar dynamical systems, the so-called ensemble control, arise in diverse emerging applications and pose a grand challenge in systems science and control engineering. Owing to the severely under-actuated nature and the difficulty of placing large-scale sensor networks, ensemble systems are limited to being actuated and monitored at the population level. Moreover, mathematical models describing the dynamics of ensemble systems are often elusive. Therefore, it is essential to design broadcast controls that excite the entire population in such a way that the heterogeneity in system dynamics is robustly compensated. In this article, we propose a reinforcement learning (RL)-based data-driven control framework incorporating population-level aggregated measurement data to learn a global control signal for steering a dynamic population in the desired manner. In particular, we introduce the notion of ensemble moments induced by aggregated measurements and derive the associated moment system to the original ensemble system. Then, using the moment system, we learn an approximation of optimal value functions and the associated policies in terms of ensemble moments through RL. We illustrate the feasibility and scalability of the proposed moment-based approach via numerical experiments using a population of linear, bilinear, and nonlinear dynamic ensemble systems. We report that the proposed method achieves the desired control objectives of various ensemble control tasks and obtains significantly better averaged-reward when compared with three existing methods.

Investigation of the failure behaviour of equiatomic AlCrCuNiFe based high entropy alloy TIG weld cladding

The present research focuses on the development of an equiatomic AlCrCuNiFe high entropy alloy (HEA) cladding using the TIG welding process. The influence and contribution of TIG cladding process parameters (welding current, welding speed, and SOD) on the microhardness and flexural stress have been investigated using the response surface methodology (RSM) and analysis of variance (ANOVA), respectively. The indentation fracture toughness test has been conducted over the claddings exhibiting minimum, maximum, and optimum microhardness values. The result revealed that welding current has the highest effect on the mechanical performance of the HEA cladding. The FE-SEM, optical microscopy, XRD and EDS analysis were performed to characterize the claddings. The cladding deposited at the S12 test run offered the least flexural resistance, microhardness, and indentation fracture toughness which was about 812 MPa, 494.97 HV0.3, and 1.29 ± 0.04 MPa.m1/2, respectively. In contrast, the optimized S5 cladding specimen exhibited good flexural resistance, appreciable microhardness and indentation fracture toughness of about 1543.92 MPa, 712 HV0.3, and 1.63 ± 0.03 MPa.m1/2 respectively. The optimum cladding exhibited flexural stress, microhardness, and fracture toughness enhancement of 46.77 %, 30 %, and 26.35 % compared to the least resistance cladding (S12) due to the high-retained alloying elements leading to enhanced solid solution strengthening. The three-point bend-tested cladding specimens’ optical microscopy images revealed that the cladding failure was primarily due to cohesive failure, crack progression, and delamination.

Population dynamics of the flathead grey mullet (Mugil cephalus Linnaeus 1758) in the Narmada estuary on the west coast of India

The population dynamics of flathead grey mullet, Mugil cephalus, were evaluated using length-frequency data collected from the Narmada estuary monthly between January 2018 and December 2019. The population parameters were assessed and calculated using the FiSAT-II software tool (FAO-ICLARM Stock Assessment Tools) after the estimation data were divided into 10 mm class intervals. A total of 419 specimens were collected, with an average length range of 171.14±58.15 mm and an average weight of 58.15±45.65 g, respectively. The length-weight relationships were established as W = 0.0105 L2.979. Length-frequency data revealed that length groups 140–149 (15.75 %) and 130–139 mm (13.37 %) dominated the commercial catches of M. cephalus at the Narmada estuary. Different growth parameters were estimated as L∞ = 309.75 mm, K = 0.41 yr−1 and t0 = −0.0013 years. The estimated values of mortality coefficients Z, M, and F were 1.06 yr−1, 0.52 yr−1 and 0.54 yr−1, respectively. The value of exploitation ratio (E) by using the length-converted catch curve method was established as 0.51, which was found to be a little high from the optimum value (0.50). The recruitment was found to be continuous almost throughout the year, with two peaks, once during February to May and another during August to October. The maximum sustainable yield (MSY) was estimated at 970.218 t if the suggested length at first capture (Lc = 123.44 mm TL) is maintained throughout the capture periods. The fishing pressure was found to be slightly excessive for the current stocks, which should be considered for proper management of the fishery in the Narmada estuary.

Investigation on aeration efficiency and energy efficiency optimization in recirculating aquaculture coupling CFD with Euler-Euler and species transport model

Aeration plays a crucial role in maintaining the overall health and sustainability of the recirculating aquaculture system. Conventional aeration operates continuous and consumes 35.06 % of equipment energy consumption. How to optimize aeration while ensuring normal fish growth remains a great challenge. To address these challenges, this research paper established a coupled model of computational fluid dynamics and species transport model (CFD-STM). Aeration tests were carried out in a 33.3 m3 culture tank using a 0.31 m diameter aerator to find out the impacts of operating variables (aeration flow rate, bubble diameter, inlet velocity, and aerator position) on oxygen transfer using a microporous aeration system. A CFD-STM model was coupled to obtain the optimum values of aeration parameters. Results revealed that the Standard Aeration Efficiency (SAE) reductions by 32.1 % for 50 % larger aeration flow rate and 86.4 % for 50 % larger bubble diameter, as well as SAE achieved 5.67 kg/kWh with an aerator position scale factor equal to 0.48. In addition, the energy consumption of the Roots blower was compared for a single aeration and dissolved oxygen saturation. Results indicated that smaller bubbles microbubbles can effectively improve oxygen transfer coefficient. The energy consumption was reduced by 15.7 % for 50 % larger aeration flow rate and 38.6 % for 50 % lower bubble diameter. Based on the results, this study demonstrated that CFD is a useful tool to optimize the aeration system design and operation in order to enhance aeration efficiency. It provides a theoretical foundation the future optimization of energy conservation through intermittent aeration practices.

Polypyrrole nanoclips-coated Co/C nanostructures with enhanced microwave absorption

There is a challenge to boost the electromagnetic wave absorption performance of magnetic carbon-based materials by rationally adjusting the structure of MOF. In this paper, nanoclip-shaped polypyrrole (PPy) was wrapped on the surface of the Co/C nanoparticle derived from Co-MOF via in-situ polymerization of pyrrole with the help of cetylammonium bromide. The electromagnetic parameters of prepared Co/C@PPy can be easily tuned by adjusting the feed ratio of PPy to Co/C. The optimum value of reflection loss (RL) for Co/C@PPy is −45.49 dB at 2.4 mm and 15.12 GHz when the feed ratio of pyrrole to Co/C is 200 μL to 80 mg, and the effective absorption bandwidth (EAB, RL ≤ −10 dB) reaches 5.97 GHz (11.76–17.73 GHz) at the matching thickness of 2.5 mm. This study paves the way for further design and preparation of the nanocomposites based on MOF-derived nanostructures with excellent EMW absorption performance.

Mechanical Properties of End-of-life Waste Tyres Steel Fibre Reinforced Concrete: RSM-Based Modelling and Optimisation

The influence of fibres on the compressive strength of concrete is complex and is determined by the type, quantity, and characteristics of the fibres utilized in designing and forming the concrete. Designing fibre reinforced concrete (FRC) constituents is challenging and affects the concrete's performance and practicality. This study utilized response surface methodology (RSM) to optimize the properties of concrete containing steel fibre extracted from end-of-life tyres (ELTs). Face-cantered central composite design (FC-CCD) of RSM was used in the design of experiments (DOE) with aspect ratio (10-70) and volume fraction (0.5 % - 1.2 %) as the input variables. Three levels of each variable were used in forming the design matrix. The resulting concretes were then tested for slump and compressive strength at 7 and 28 days of curing. As the aspect ratio and volume fraction increased, slump values decreased, and 7- and 28-day compressive strength increased up to an aspect ratio of 45 and 1.0% volume fraction. Beyond an aspect ratio of 45 and 1.0% volume fraction, a declining trend in compressive strength at all curing ages was observed. The Analysis of Variance (ANOVA) revealed that the variables volume fraction and aspect ratio significantly affect the variability in the FRC models, with all models being statistically significant at the 95% level across all factor levels. A numerical optimization method was used to determine the optimal mix proportions for ELTFRC. The optimum response values were achieved by combining a 1.01% volume fraction and a 33.86 aspect ratio, resulting in a desirability of 0.73.

Optimization and evaluation of thermo-hydraulic performance of solar air heaters equipped with different roughness geometries

Solar air heaters have poor thermal efficiency. Applying artificial roughness elements is a common approach to enhance thermal performance of a solar air heater. In this study, fluid flow and heat transfer characteristics of solar air heaters equipped with six different roughness geometries, including inline cubical elements, staggered cubical elements, rectangular baffles, square ribs, V-shaped baffles, and V-shaped ribs, were assessed. First, parameters of each roughness geometry were optimized and then thermo-hydraulic performance of solar air heaters having optimum roughness geometries was compared. For each roughness geometry, two design parameters were selected and 25 numerical experiments were designed based on the full factorial design of experiment approach. The thermo-hydraulic performance parameter (THPP) was set out as the objective function and its relationship with roughness parameters was defined using the non-parametric regression response surface method. Finally, the optimum values of roughness parameters were determined using the nonlinear programming by quadratic Lagrangian (NPQL) technique. Comparing the performance of solar air heaters having optimum roughness geometries showed that rectangular baffles led to the largest and inline cubical elements brought about the smallest THPP at almost all Reynolds numbers. The maximum THPP of rectangular baffles was 2.28 at the Reynolds number of 25,000.

Optimizing a vertical axis wind turbine with helical blades: Application of 3D CFD and Taguchi method

Vertical axis wind turbines (VAWT) with helical blades are being interested in urban areas due to the low dependency on the wind direction and also due to low noise production. In spite of good self-starting and smooth operation as the advantages of this type of turbine, its optimizing has not been reported in literature yet and is the subject of this research. A primary investigation by 3D computational fluid dynamics (CFD) to simulate the air flow about the above type of VAWT in the first step showed the importance of three design variables of tip speed ratio, helical angle, and airfoil chord length. However, in the second step, CFD runs were very time consuming and costly. For considering five values for each design variable, 53=125 cases had to be simulated by 3D CFD. Thus, by the use of Taguchi method and with definition of an orthogonal array named L-25, the number of required 3D CFD simulations reduced from 125 to 25. Then, the optimum values of variables were obtained as 1.33 for the tip speed ratio, 30 degrees for the helical angle, and 0.4 m for the airfoil chord length. At the optimum point, the average power coefficient increased to 0.17542. The presented research is novel for optimizing VAWT with helical blades.

Mechanical durability and degradation characteristics of long kenaf-reinforced PLA composites fabricated using an eco-friendly method

The research has focused on evaluating the mechanical strength of long unidirectional kenaf-PLA composite and the degradation effect after accelerated weathering exposure. The composite was fabricated using a green traditional method of hot-pressing method with different compositions of non-chemically treated kenaf fiber to PLA with the ratio of 30:70, 40:60, 50:50, and 60:40. The finding of this work demonstrated fiber weight-matrix ratio of 50:50 gain a remarkable optimum strength with 224 % and 239 % enhancement for tensile and flexural strength respectively. The applied enhanced Rule of Mixture (E-ROM) model used in this study was reliable in predicting the experimental results whereby the composites reached an optimum value. Despite the successful enhancement of fiber–matrix compatibility and composite strength, the presence of fiber misalignment, structural imperfections, and voids observed in scanning electron microscopy (SEM) images still has some detrimental effects on its degradability. Nevertheless, a noteworthy strength reduction of PLA (80 %) as compared to the PLA-kenaf composite demonstrated better resistivity of weathering for the composite after 508 h. Hence, this study suggests deeper insights into the viability of eco-friendly combed methods as fiber treatments for kenaf as PLA reinforcement. It is proven that even without any chemical treatment, the composite demonstrated a remarkable mechanical resilience suitable for indoor non-structural applications.

Poly(ether ketone ketone)/hollow silica filler substrates and application for sixth generation communication

AbstractThe influence of density and shape of hollow silicas (silica hollow tubes (SHT) or hollow glass microspheres (HGM)) on dielectric and heat‐resisting characteristics of poly(ether ketone ketone) (PEKK)/SHT and PEKK/HGM films was systematically investigated. The dielectric characteristics of PEKK/SHT or PEKK/HGM films decrease to a minimum, as their SHT or HGM contents approach 8 or 20 wt%, respectively, and the minimum dielectric constant (εr) and dielectric loss (tan δ) of PEKK/SHT or PEKK/HGM films decrease visibly with decreasing SHT densities. By filling with 0.46 g cm−3 mean density of SHT or HGM fillers, the minimum εr and tan δ of PEKK/SHT films are somewhat smaller than those of PEKK/HGM films. The linear coefficient of thermal expansion (LCTE) values of PEKK/SHT or PEKK/HGM films reduce and their onset degradation temperature values increase gradually with increasing SHT and HGM contents. Low εr/tan δ (2.11/0.0022 and 2.2/0.0027 at 1 MHz), LCTE (35.5 × 10−6 and 31.2 × 10−6 °C−1) and excellent heat‐resisting properties favorable for sixth generation (6G) ultrarapid communication are realized for appropriately prepared PEKK/SHT and PEKK/HGM substrate films. The free‐volume‐hole characteristics of PEKK/HGM or PEKK/SHT films approach a maximum as SHT or HGM contents reach an optimum value of 8 or 20 wt%, respectively. © 2024 Society of Chemical Industry.

Machine learning optimization of a zero energy building (ZEB) by waste heat recovery in a co-generation system

Waste heat recovery is known as one of the newest methods to increase the energy and exergy efficiencies of thermal systems. The concept has been also developed in co-generation systems where productions of electricity, cooling and heating are considered, simultaneously. This study investigates tri-generation system with a co-generation ammonia-water cycle (AWC) and an organic Rankine cycle (ORC) to supply cooling, heating, and power, simultaneously. As novelty, six different working fluids of Ammonia, R124, R134a, R152a, R245fa and R290 were investigated, as six scenarios for ORC. As result, the R124 was found as the best one and the next investigations were followed just with this fluid. Minimizing total cost and maximizing exergy efficiency at the same time are the main aim of this paper. Design Expert software was used to find out about this optimum point. Four cities in Australia were selected and the effect of annual ambient temperatures of them were studied on the outputs of the system, using TRNSYS software. Also, the optimum values of the effective parameters, including the isentropic efficiencies of pumps, compressor, and turbines, heat source temperature, ammonia concentration, utmost pressures of ORC and AWC, were obtained and reported by means of TOPSIS. As results, the monthly generations in electricity, heating and cooling were estimated. It was revealed that the lower ambient temperature, the higher electricity generation, according to the thermodynamic cycle of the proposed co-generation system. Finally, the results showed that application of the suggested co-generation system can cover the electricity demand of 294 apartments, heating for 208 apartments, and cooling load for 18,999 apartments.

Highly sensitive optical thermometer based on Li+-doped erbium ytterbium silicate films

This work provides a new material, Li+-doped erbium ytterbium silicate, with reliable and high temperature sensing sensitivity for optical thermometers. The Li+-doped erbium ytterbium silicate films were prepared by RF magnetron sputtering. The film shows strong green visible emission, which is related to the radiation transitions from thermally coupled energy levels 2H11/2, 4S3/2 to 4I15/2. Lithium doping reduces the field symmetry around erbium and improves the radiation transition efficiency, and Yb doping improves the pump light absorption. Lithium or ytterbium is advantageous to enhance up-conversion luminescence and temperature sensing sensitivity of erbium ions. Therefore, the temperature dependence of green up-conversion luminescence in the range of 80–460 K of the Li+-doped erbium ytterbium silicate films have been found to be a promising optical thermometer material with a high relative sensitivity of 16.9 % K−1, which is higher than the optimum value of other Er3+ materials. In addition, we have provided a preliminary temperature measurement scheme based on the Li+-doped erbium ytterbium silicate film.

Next-generation lung cancer pathology: Development and validation of diagnostic and prognostic algorithms

Non-small cell lung cancer (NSCLC) is one of the most common malignant tumors. In this study, we develop a clinically useful computational pathology platform for NSCLC that can be a foundation for multiple downstream applications and provide immediate value for patient care optimization and individualization. We train the primary multi-class tissue segmentation algorithm on a substantial, high-quality, manually annotated dataset of whole-slide images with lung adenocarcinoma and squamous cell carcinomas. We investigate two downstream applications. NSCLC subtyping algorithm is trained and validated using a large, multi-institutional (n= 6), multi-scanner (n= 5), international cohort of NSCLC cases (slides/patients 4,097/1,527). Moreover, we develop four AI-derived, fully explainable, quantitative, prognostic parameters (based on tertiary lymphoid structure and necrosis assessment) and validate them for different clinical endpoints. The computational platform enables the high-precision, quantitative analysis of H&E-stained slides. The developed prognostic parameters facilitate robust and independent risk stratification of patients with NSCLC.

Desalination Performance Evaluation of hBN@UiO‐66 MOF Incorporated Hollow Fiber Membrane using Membrane Distillation

AbstractIn the present study, the polyvinylidene fluoride (PVDF) modified hollow fiber (HF) membranes have been synthesized by dry‐jet wet phase inversion technique for desalination via direct contact membrane distillation (DCMD). UiO‐66 metal‐Organic framework (MOF) and surface‐modified UiO‐66 with h‐BN nanomaterial have been incorporated with PVDF‐HF membrane. The various analytical methods like FE‐SEM, FTIR, PXRD, TGA, tensile strength, were used to characterize prepared MOF and HF membranes. It was observed from DCMD results that the h‐BN@UiO‐66/PVDF (M3) HF membrane gives higher flux than UiO‐66/PVDF (M2) and pure PVDF (M1) membrane. In the present investigation, response surface methodology (RSM) by box‐behnken design (BBD) was used to optimize the DCMD system and evaluate the behavior of operating parameters such as feed temperature, feed concentration, and feed flow rate. The optimum operating values of feed temperature, feed flow rate, and feed concentration were found as 65 °C, 1 LPM, and 1.5 wt.%, respectively. Under optimum conditions, the M3 HF membrane gives the highest flux of 26.78 L/m2 ⋅ h compared to M2 and M1 membranes. During DCMD, salt rejection is almost greater than 99.8 % for all prepared membranes. Other salts like potassium chloride (KCl), Magnisium Sulphate (MgSO4), and calcium sulphate (CaSO4) are also used to check salt rejection of all membranes at optimum operating conditions. Moreover, the M3 membrane exhibits stable permeate flux and high salt rejection (>99.9 %) at optimized process parameters over 80 h running. Overall, the present study provides the positive effects of h‐BN nanoparticles and UiO‐66 MOF on HF membrane to improve performance in DCMD.