Module Placement Research Articles

Hydrodynamic and energy-harvesting performances of a compact-array OWC device: An experimental study

This study proposed and evaluated a compact-array oscillating water column device called “Rainbow”, which is expected to adapt to the nearshore wave climate characteristics in China, and satisfy the quick response requirements to coastal and offshore electricity. The proposed device employs a bent-duct buoy concept, with an open mouth in two opposite directions, and embraced a moon pool in a ring shape. Moreover, models under a scale ratio of 1:10 are designed and manufactured, which are fixed on the bottom and tested in regular and irregular wave scenarios in a wave tank. The time histories of critical performance parameters, hydrodynamic and aerodynamic performances, and capture width ratios in different modules were recorded and analyzed to reveal the operating mechanisms of the model. The various module arrangement topologies were found to cause significant differences in the value distribution characteristics of the capture width ratio in different modules, peaking at 0.26. The experimental results can be employed as critical benchmark cases for further designs and optimizations.

SIMULATION OF BIOMASS CARBONIZATION AND HEAT RECOVERY FOR ELECTRICITY PRODUCTION USING SEEBECK MODULES

This paper experimentally and numerically examines biomass carbonization and using thermoelectric modules to recover heat and generate electricity using the finite element method (FEM). Carbonization, conducted at high temperatures, produces charcoal and gases. The study identifies optimal temperatures for module placement, demonstrating that they can generate up to 6.5 W of electricity. Moreover, integrating the chimney to optimize the carbonization process produced hot air with a maximum gain of 11 °C above the ambient temperature. This approach enhances energy efficiency and reduces costs

(Invited) Challenges and Opportunities in the Integration of CO2 Capture and Conversion

Over the past 15 years or so, the electroreduction of CO2 for the synthesis of intermediates for the chemical industry as well as for the synthesis of small molecule fuels has become a very active area of research. Activities span the whole value chain from electro-catalyst synthesis and characterization, to electrode and cell design and optimization, as well as studies on techno-economic feasibility and life cycle assessment of envisioned overall processes. In the vast majority of these studies, >98% pure CO2 is being used as the feed. In reality, CO2 feeds captured from industrial flue gasses or even those captured from air may not be close to 100% in CO2.This presentation will explore the integration of a nanomembrane-based CO2 capture method with different CO2 electrolysis approaches. The nano-membrane-based CO2 capture is based on work by Fujikawa and Selyanchyn (Polymer J. 2021, 53, 111), using polydimethylsiloxane-derived membranes that are preferentially permeable to CO2 over nitrogen. Multistage arrangement of modules comprised of arrays of membranes allows for efficient concentration of CO2 from air, from 420 ppm to tens of percent, using just vacuum pumps to drive the process. We explore the use of these concentrated CO2 streams that still contain nitrogen and some oxygen as the feed in subsequent CO2 electrolysis conversions to CO, to methane, or to other products, depending on catalyst choice. We study the effects of feed composition, catalyst chosen, and cell configuration on product speciation, conversion efficiency, etc.

Variation of the 3'RR1 HS1.2 Enhancer and Its Genomic Context.

In humans, the HS1.2 enhancer in the Ig heavy-chain locus is modular, with length polymorphism. Previous studies have shown the following features for this variation: (i) strong population structuring; (ii) association with autoimmune diseases; and (iii) association with developmental changes in Ig expression. The HS1.2 region could then be considered as a contributor to inter-individual diversity in humoral response in adaptive immunity. We experimentally determined the HS1.2-length class genotype in 72 of the 1000 Genomes CEU cell lines and assigned the HS1.2 alleles to haplotypes defined by 18 landmark SNPs. We also sequenced the variable portion and ~200 bp of the flanking DNA of 34 HS1.2 alleles. Furthermore, we computationally explored the ability of different allelic arrangements to bind transcription factors. Non-random association between HS1.2 and Gm allotypes in the European population clearly emerged. We show a wealth of variation in the modular composition of HS1.2, with five SNPs further contributing to diversity. Longer alleles offer more potential sites for binding but, for same-length alleles, SNP variation creates/destroys potential binding sites. Altogether, the arrangements of modules and SNP alleles both inside and outside HS1.2 denote an organization of diversity far from randomness. In the context of the strong divergence of human populations for this genomic region and the reported disease associations, our results suggest that selective forces shaped the pattern of its diversity.

Early-Detection of EVA Encapsulant Degradation in PV Modules Based on Vibration Frequency Analysis

In engineering applications, the frequency analysis represents a first and practical step to collect relevant parameters for structural and mechanical diagnostics. Any possible material / component degradation and deterioration can be prematurely detected by frequency modifications that exceed a certain alert value. In this paper, the attention is given to the dynamic mechanical analysis of commercial photovoltaic (PV) modules, in which the solar cells are typically encapsulated in thin viscoelastic interlayers made of Ethylene-Vinyl Acetate (EVA), which are primarily responsible for the load-bearing capacity of the sandwich PV system. As a major effect of ageing, ambient conditions, non-uniform / cyclic thermal gradients, humidity and even extreme mechanical / thermal loads, the rigidity of these films can largely modify and decrease, thus possibly affecting the mechanical capacity of the PV module, and even exposing the solar cells to fault. Knowledge of the effective bonding level is an important step for diagnostic purposes. In this regard, the present study is based on a preliminary but extensive parametric Finite Element (FE) numerical investigation of full-scale commercial PV modules of typical use in buildings. The attention is given – for PV module arrangements of technical interest – to the effect of EVA stiffness in terms of vibration modes and especially frequency sensitivity. As shown, when compared to newly installed PV modules, any kind of stiffness decrease is associated to major frequency modifications for the composite system, and in the worst configuration, such a frequency scatter can decrease down to -40% the original condition. Such a marked stiffness decrease would be implicitly associated to a weak mechanical performance of the sandwich section, with major stress peaks and deflections in the PV system, even under ordinary loads. The presented results, in this sense, suggest that major consequences can be prevented and minimized by monitoring the vibration frequency of PV modules.

Non-destructive vibration-based monitoring analysis of PV modules with encapsulant degradation by frequency change

In engineering applications, the frequency analysis represents a first and practical step to collect relevant parameters for structural and mechanical diagnostics. Any possible material / component degradation and deterioration can be prematurely detected by frequency modifications that exceed a certain alert value. In this paper, the attention is given to the dynamic mechanical analysis of commercial photovoltaic (PV) modules, in which the solar cells are typically encapsulated in thin viscoelastic interlayers made of Ethylene-Vinyl Acetate (EVA), which are primarily responsible for the load-bearing capacity of the sandwich PV system. As a major effect of ageing, ambient conditions, non-uniform / cyclic thermal gradients, humidity and even extreme mechanical / thermal loads, the rigidity of these films can largely modify and decrease, thus possibly affecting the mechanical capacity of the PV module, and even exposing the solar cells to fault. Knowledge of the effective bonding level is an important step for diagnostic purposes. In this regard, the present study is based on a preliminary non-destructive experimental analysis, and on an extensive parametric Finite Element (FE) numerical investigation of full-scale commercial PV modules of typical use in buildings. The attention is given – for PV module arrangements of technical interest – to the effect of EVA stiffness in terms of vibration modes and frequency sensitivity. As shown, especially compared to newly installed PV modules, any kind of stiffness decrease is associated to major frequency modifications for the composite system, and in the worst configuration, such a frequency scatter can decrease down to −40% the original condition. Such a marked stiffness decrease would be implicitly associated to a weak mechanical performance of the sandwich section, with major stress peaks and deflections in the PV system, even under ordinary loads. The presented results, in this sense, suggest that major consequences can be prevented and minimized by monitoring the vibration frequency of PV modules.

Improved optimization algorithm for resource management in cloud applications with performance monitor of VM provisioning, placement and recycling

The machine learning technique has been used to increase cloud management’s intelligence. Effective resource provisioning also preserves the environment. Manual cloud management has some difficult problems, such as complexity in cloud systems and scale issues. Hence, this paper introduces a new task for managing the resources in the cloud using deep learning. The aim is to predict the overall workload and server status prediction to the cloud resource management. Initially, performance monitoring is performed to keep aware of the performance of the application and guarantee the cloud application’s performance. In the suggested work, the required data is collected for the resource utilization on multiple Virtual Machine (VM) metrics. The VM provisioning is performed next to rectify the issues of resource provisioning. After that, the workload and server status prediction is conducted, where the Weighted Recurrent Neural Network (W-RNN) is adopted. After attaining the predicted workload, the VM placement module is carried out. Here, the virtual resource’s quantity is attained. Moreover, the multi-objective functions like resource utilization; cost, energy, time, and Quality of Service (QoS) are derived in this phase with the help of the Improved Rain Optimization Algorithm (IROA). Subsequently, the VM recycling is performed in the suggested work. Here, a resource collector is given for the virtual resources recycling task. It scans the applications of the cloud in the data centre and processes the VM recycling for every application. While considering the statistical analysis of the IROA-W-RNN-based resource management system achieved a mean of 56.27% than JAYA-W-RNN, 21.09% than SCO-W-RNN, 60.2% than MFOA-W-RNN, and 16.74% than DA-W-RNN for configuration 4. Finally, the numerical analysis is conducted to validate the presented resource management task with the aid of various conventional tasks.

Development and Validation of USM-Insulin Adherence Module for Patients with Type 2 Diabetes Mellitus.

Many patients with type 2 diabetes mellitus (T2DM) do not achieve the desired glycaemic control despite being treated with insulin. Studies found this due to an improper understanding of insulin function, its intensification process and patients' negative perspective on insulin. We developed an education module to enhance adherence to insulin therapy. This study applied a mixed design. It was conducted in three phases: i) Phase I: literature search and focus group discussions (FGDs), ii) Phase II: module development and iii) Phase III: content and face validation of Universiti Sains Malaysia-Insulin Adherence Module (USM-IAM). FGDs were used to gather patients' opinions. All researchers repeatedly discussed about the module content and arrangement, the words and images used, and the grammar in producing the final draft. Specialists and target audience performed content and face validation of the module. Thirty-six participants were involved in the FGDs. Data saturation was achieved at the 4th FGD. Three themes emerged from qualitative data analysis and were incorporated into the module. USM-IAM was finalised with five units. The content validity index (CVI) was 0.92, while face validity agreements were between 86% and 97%. The CVI and face agreement for USM-IAM exceed the cut-off point for a sound module. It has good potential to be used as a resource for educating patients in enhancing insulin adherence.

Falcon optimiser for optimal design of Even-Odd column for shaded PV array reconfiguration

ABSTRACT The operation of Photo-Voltaic (PV) arrays is often negatively impacted by the presence of shadows, which significantly reduce the generated power. Shadows create multiple local Maximum Power Points (MPP) and a unique global MPP within the shaded array. Therefore, it is crucial to distribute shadows evenly across the PV array surface to optimise power extraction by reconfiguring the shaded modules. This paper presents a novel approach for reconfiguring Solar Photovoltaic (PV) systems using the Falcon optimiser, specifically focusing on Even-Odd column reconfiguration. The objective is to optimise the performance of the PV system by strategically rearranging the columns of PV modules. The proposed technique aims to minimise shading effects and maximise power generation efficiency by identifying the optimal placement of modules. The Falcon optimiser, known for its efficient and effective optimisation capabilities, is utilised to determine the optimal configuration. Extensive simulations and comparisons with other reconfiguration methods are conducted to evaluate the performance of the proposed approach. The results demonstrate that the Even-Odd column reconfiguration based on the Falcon optimiser significantly improves the overall power output and efficiency of the solar PV system compared to alternative methods.

Efficient Resource Scheduling in Fog: A Multi-Objective Optimization Approach

Fog computing is a novel idea that extends cloud computing by offering services like processing, storage, analysis, and networking on fog devices closer to IoT devices. Numerous fog devices are required to process the ever-growing amount of data generated by IoT applications. The heterogeneous tasks from various IoT applications compete for a limited number of resources of these devices. The process of assigning this set of tasks to different available fog nodes according to QoS requirements for processing is resource scheduling. Resource scheduling aims to optimize resource utilization and performance metrics however, the dynamic nature of the Fog environment, resource-constrained, and heterogeneity in fog devices make resource scheduling a complex issue. This research presents the design and implementation of a multi-objective optimization-based resource scheduling algorithm using Modified Particle Swarm Optimization (MPSO) that addresses the application module placement and task allocation issues. This two-step MPSO-based resource scheduling model finds the optimal fog node to place each application module and assigns appropriate tasks to the most optimal fog nodes for execution. The proposed model unlocks the full potential of fog resources along with maximization of overall system performance in terms of optimization of cost, latency, energy consumption, and network usage. The simulation results indicate that using MPSO energy consumption is reduced by 53.94% and 43.58% as compared to First Come First Serve (FCFS) and Particle Swarm Optimization (PSO), respectively. The loop delay, network usage and cost using MPSO are reduced by 40.3%, 67.69% and 90.01% respectively, as compared to PSO algorithm.

An energy-aware module placement strategy in fog-based healthcare monitoring systems

Fog computing and the Internet of Things (IoT) have revolutionized healthcare monitoring systems, enabling real-time health data collection and transmission while overcoming cloud computing limitations. However, efficiently selecting fog nodes for application modules with varying deadline requirements and ensuring adherence to quality of service (QoS) criteria pose significant challenges due to resource constraints and device limitations. In this paper, we present a novel two-layered hierarchical design for fog devices, leveraging cluster aggregation to optimize the selection of fog nodes for healthcare applications. We introduce three efficient algorithms to minimize system latency and reduce energy consumption in fog computing environments. Our proposed model is rigorously evaluated using the iFogSim toolkit and compared with cloud-based and latency-aware model [Mahmud R, Ramamohanarao K, Buyya R in ACM Transactions on Internet Technology.19, 2018, 10.1145/3186592]. In four distinct network topologies, our model exhibits an average latency reduction of at least 87% and energy consumption reduction of at least 76% when compared to the Cloud-based model. Similarly, when compared to the Latency-aware model proposed in [Mahmud R, Ramamohanarao K, Buyya R in ACM Transactions on Internet Technology. 19, 2018, 10.1145/3186592], our model showcases a minimum reduction of 43% in average latency and 27% in energy consumption. Our contribution lies in addressing the complexity of selecting fog nodes for application modules with diverse deadline requirements, while ensuring QoS. This work advances the field of real-time healthcare monitoring systems, promising substantial improvements in efficiency and effectiveness.

Differences in kinetic factors affecting gait speed between lesion sides in patients with stroke.

The differences in kinetic mechanisms of decreased gait speed across brain lesion sides have not been elucidated, including the arrangement of motor modules reflected by kinetic interjoint coordination. The purpose of this study was to elucidate the differences in the kinetic factors of slow gait speed in patients with stroke on the lesion sides. A three-dimensional motion analysis system was employed to assess joint moment in the lower limb and representative gait parameters in 32 patients with right hemisphere brain damage (RHD) and 38 patients with left hemisphere brain damage (LHD) following stroke as well as 20 healthy controls. Motor module composition and timing were determined using principal component analysis based on the three joint moments in the lower limb in the stance phase, which were the variances accounted for principal components (PCs) and the peak timing in the time series of PCs. A stepwise multiple linear regression analysis was performed to identify the most significant joint moment and PC-associated parameter in explaining gait speed. A negligible difference was observed in age, weight, height, and gait speed among patients with RHD and LHD and controls. The following factors contributed to gait speed: in patients with RHD, larger ankle plantarflexion moment on the paretic (p = 0.001) and nonparetic (p = 0.002) sides and ankle dorsiflexion moment on the nonparetic side (p = 0.004); in patients with LHD, larger ankle plantarflexion moment (p < 0.001) and delayed peak timing of the first PC (p = 0.012) on the paretic side as well as ankle dorsiflexion moment on the nonparetic side (p < 0.001); in the controls, delayed peak timing of the first PC (p = 0.002) on the right side and larger ankle dorsiflexion moment (p = 0.001) as well as larger hip flexion moment on the left side (p = 0.023). The findings suggest that the kinetic mechanisms of gait speed may differ among patients with RHD following patients with stroke with LHD, and controls.

Techno-economic analysis of commercial-scale 15 MW on-grid ground solar PV systems in Bakalia: A feasibility study proposed for BPDB

Renewable energy systems, such as solar power, are becoming increasingly important worldwide due to the limited supply of non-renewable energy sources. Solar power stands out as a practical choice for electricity generation because it's simple to set up and costs less than other renewable options. Solar-based on-grid or grid-tied systems are more effective compared to other PV grid systems due to their more reasonable installation system, favorable maintenance, and less complex system. This study was designed with a solar-based grid-tied system. This study evaluates the performance and economic viability of a 15 MW on-grid photovoltaic (PV) system in Bakalia Char, Chittagong, Bangladesh, and will propose this study for the Bangladesh Power Development Board (BPDB). Developing a clean energy system in Bangladesh is the main purpose of this study. This system performed efficiently, with an 84.03 % performance ratio, produced energy of 21,510.186 MWh/year and proved economically attractive with a 4.5-year payback period, a competitive electricity cost of 0.024 USD/kWh, and a 389 % return on investment(ROI%). Using software like PVsyst and SketchUp ensures precise system design and optimal module placement. Also, use a better PV panel system whose efficiency is higher than that of another PV panel system designed for a similar project. This system boosts local electricity production and aligns with sustainable energy goals. Its success is a valuable model for future solar projects in similar regions facing energy challenges. The system also shows significant environmental benefits, with a projected reduction of approximately 252,168.5 tons of CO2 emissions over its operational lifespan.

Altered foot placement modulation with somatosensory stimulation in people with chronic stroke

Many individuals who experience a stroke exhibit reduced modulation of their mediolateral foot placement, an important gait stabilization strategy. One factor that may contribute to this deficit is altered somatosensory processing, which can be probed by applying vibration to the involved muscles (e.g., the hip abductors). The purpose of this study was to investigate whether appropriately controlled hip abductor vibration can increase foot placement modulation among people with chronic stroke. 40 people with chronic stroke performed a series of treadmill walking trials without vibration and with vibration of either the hip abductors or lateral trunk (a control condition) that scaled with their real-time mediolateral motion. To assess participants’ vibration sensitivity, we also measured vibration detection threshold and lateral sway evoked by abductor vibration during quiet standing. As a group, foot placement modulation increased significantly with either hip or trunk vibration, compared to without vibration. However, these changes were quite variable across participants, and were not predicted by either vibration detection threshold or the lateral sway evoked by hip vibration during standing. Overall, we found that somatosensory stimulation had small, positive effects on post-stroke foot placement modulation. Unexpectedly, these effects were observed with both hip abductor and lateral trunk vibration, perhaps indicating that the trunk can also provide useful somatosensory feedback during walking. Future work is needed to determine whether repeated application of such somatosensory stimulation can produce sustained effects on this important gait stabilization strategy.

Enhanced ARIA-based counter mode deterministic random bit generator random number generator implemented in verilog

This paper presents a study aimed at effectively implementing a deterministic random bit generator (DRBG) IP in verilog language, based on the standard encryption algorithm. By controlling the existing round generation and key generation blocks, the internal modules of the counter mode deterministic random bit generator (CTR-DRBG) were successfully implemented and operated, ensuring the secure and efficient generation of random bit sequences. The research focused on parallel operation of modules and optimized module placement to achieve improved clock frequencies. By concurrently operating two modules in the derivation and internal update modules of CTR-DRBG, the processing speed was enhanced compared to the conventional algorithm. Additionally, integrating the reseeding and initialization modules of CTR-DRBG into a single module successfully reduced size. Furthermore, this IP supports the special function register (SFR) interface. The safety of the CTR-DRBG was validated through known answer test (KAT) verification utilizing test vectors from certification. Future research should explore additional studies on CTR-DRBG operating on real FPGA or ASIC, not only using normal algorithm but also employing other block cipher algorithms.

Automated modeling of nonlinear systems using fuzzy modular neural network

Modular neural networks (MNNs) have garnered substantial attention in the field of nonlinear system modeling. However, even though MNNs require fewer hyperparameters due to their hierarchical structure compared to traditional NNs, determining the optimal module arrangement remains challenging. To address these issues, a novel approach named fuzzy modular neural networks (FMNN) is introduced. This method employs conditional fuzzy clustering and incremental radial basis function (RBF) neural networks to automatically construct sub-modules within the MNN framework. The resultant sub-modules are chosen utilizing a distance-based fuzzy integrative strategy, effectively diminishing the necessity for manual intervention. To showcase the superiority of the FMNN approach, a series of experiments are carried out employing three benchmark examples. These experiments encompass a comparison of modeling accuracy against other extensively employed neural network models. The experimental findings illustrate that FMNN surpasses alternative neural network models in terms of model precision.

Modified Odd–Even–Prime pattern for effective dispersion of shade over the PV array under partial shading conditions

Photovoltaic (PV) arrays are extensively used for power generation being the most abundant renewable energy sources available. Partial shading of the PV array leads to mismatch between the modules and decrease in output power. A new Modified Odd–Even–Prime (MOEP) method of module arrangement for total cross tied (TCT) connection is presented in this paper to mitigate the effects of partial shading. The proposed MOEP method is a physical relocation method where the modules of PV array are relocated without altering the electrical connections. MOEP method of module rearrangement is based on row positions of the PV array. In this method, the row positions of PV modules in the first column are filled with odd, even and prime numbers in ascending order. The row positions of further columns are formed based on MOEP method. The proposed MOEP method is implemented on 9 × 9 and 8 × 8 PV arrays under various shading patterns. Further, the results are compared with TCT, Odd Even (OE) and Odd–Even–Prime (OEP) patterns. MATLAB/Simulink is used to validate the results. An experimental setup of 9 × 9 and 8 × 8 PV array is developed and tested in real time environment to validate the effectiveness of MOEP method over TCT, OE and OEP methods. The performance of proposed MOEP method is evaluated in terms of GMPP, mismatch power loss (MPL), Fill Factor (FF), Efficiency (η) and Shade Dispersion Ratio (SDR). A detailed study on energy savings from the proposed MOEP method is made on hourly, daily and yearly basis. From the experimental results obtained, it is observed that MOEP method generates a significant improvement in GMPP, efficiency and reduces the mismatch loss compared to TCT, OE and OEP methods.

POTENSI PENGHEMATAN BIAYA TAGIHAN LISTRIK PASCA PEMASANGAN PLTS ATAP GEDUNG RUSUNAWA UNIVERSITAS UDAYANA

The limited availability of natural resources and the impact on the environment that regularly using fossil-based electricity become the basis for the government to develop renewable energy. One of the potential uses of renewable energy in Indonesia is a rooftop solar power plant which in its design does not require a large area of land and space for the placement of solar modules. This research designing rooftop solar power plants with an on-grid system on the Udayana University Rusunawa building with the capacity scenario of 20 kWp, 15 kWp, and 10 kWp using HelioScope. Based on the results of rooftop solar power plants production can be seen the potential cost savings of electricity bills each year. Based on the results of the design for rooftop solar power plants scenario, the number of solar modules obtained is 40 pieces, 30 pieces and 20 pieces with potential savings in electricity bill costs of Rp. 33.391.515, Rp. 28.933.478 and Rp. 23.269.285 per year.

The visual preference and physiological response to Sierpinski triangle acoustic diffuser: An experimental study using VR technique

Diffusive surfaces should be optimally designed for acoustic and aesthetic purposes. Fractals are commonly used to adapt to the parametric demands of interface design combining mathematical calculation and artistic creation. The author’s previous study proposed an improved fractal acoustic structure based on the Sierpinski-triangle building rule. However, there is insufficient research on quantifying the physiological and visual benefits of such fractal features. Focusing on fractal iteration and randomness of module arrangement as fractal parameters, this study investigates in 3D-virtual reality meeting rooms and identifies the influence of fractal diffusion structures on visual preferences and physiological responses. Variance analysis is used to construct models of fractal parameters, subjective preference factors, and physiological indicators. Results suggest that the Sierpinski-triangle pattern iterated twice with mid-high complexity (quantified by the fractal dimension D = 1.58) is significantly more visually preferred than other fractals. When the modules are at low randomness (quantified by P = 0.25), more random module arrangements elicited better beneficial outcomes. However, as the degree of randomness increased, effects became increasingly irregular. Subjective evaluation factors and physiological indicators show a strong correlation. To sum up, the use of the medium to mid-high complexity fractals in interior spaces would help enhance the visual preferences and stress relief of occupants.

Optimizing Operational Conditions of Pilot-Scale Membrane Capacitive Deionization System

In this study, we developed a pilot-scale membrane capacitive deionization (MCDI) system for treating mildly brackish water and examined various operational parameters, including module arrangements, adsorption/desorption times, and flow rates. As we aimed to optimize these parameters to increase total dissolved solids (TDS) removal efficiency, the results revealed that the dual-series mode module arrangement and an adsorption time of 120 s with a flow rate of 10 L/min achieved the highest TDS removal efficiency of 99%. Energy consumption analysis showed that lower flow rates were associated with higher TDS removal efficiencies, highlighting the balance between energy consumption and water quality. This study provides insights into optimizing a pilot-scale MCDI for efficient water supply solutions, offering promise for sustainable and eco-friendly water treatment.