Current Operation Research Articles

Gd0.1Ce0.9O1.95 impregnated with Ba(Ce0.9Y0.1)0.8Ni0.2O3–δ@CuO as SOFC anode with increased carbon-deposition resistant for direct hydrocarbon fuels

Solid oxide fuel cells are thought to be capable of a wide range of fuels, but the direct utilization of hydrocarbon fuels increases the risk of carbon deposition at the anode. In this research, a novel full cell structure supported by porous Gd0.1Ce0.9O1.95 (GDC) skeleton is proposed and anode material Ba(Ce0.9Y0.1)0.8Ni0.2O3–δ (BCYN) is proposed to be impregnated on the skeleton to form a multi-functional anode. In the operating reductional atmosphere, Ni is reduced from the BCYN and provides electrochemical catalyticity, and BCYN can absorb water on its surface to prevent carbon deposition. 5% Cu is also applied to the anode to increase electron conductivity by impregnation, and it doubles the peak power density. The composite Ba(Ce0.9Y0.1)0.8Ni0.2O3–δ@CuO anode is evaluated and found to have good carbon deposition resistance with 3% humidified C2H6 in constant current operation for 120 h, and withstand 3% humidified CH4 in OCV condition for nearly 160 h.

Overlap function-based fuzzy β-covering relations and fuzzy β-covering rough set models

As an extension of the fuzzy covering, fuzzy β-covering has garnered significant scholarly concern. However, certain limitations impede its practical application. To address the issue of inaccurate characterization of object relationships caused by the current fuzzy β-neighborhood operator, four new operators were developed, which exhibit both symmetry and reflexivity through the utilization of established fuzzy β-neighborhood operators, overlap functions and grouping functions. Furthermore, we demonstrate that these operators satisfy the fuzzy β-covering relation, and utilize the fuzzy β-covering relations on the basis of overlap functions to propose new fuzzy β-covering rough set model. Additionally, incorporating the attribute significance, an attribute reduction algorithm is designed. Ultimately, we substantiate the rationality and superiority of our proposed algorithm by conducting a sequence of experiments. Meanwhile, we analyze the impacts of varying overlap functions and β values on the algorithm's performance.

Public–Private Partnering in Nuclear Reactor Development—Historical Review and Implications for Today

Abstract The dominant nuclear reactor technologies that comprise the current global operating fleet were developed and deployed over a relatively short, midtwentieth century, period spanning the 1950s and 60 s. Four of these technologies were deployed at fleet scales and commercially exported. The historical record indicates a remarkably consistent process of phased technology development that enabled the commercialization of designs that would define the global nuclear marketplace, beginning with research and development (R&D) and advancing through test reactors, small and large demonstration reactors, and first commercial-scale units. Following proof-of-principle R&D, historical commercialization lead times (from decision to construction of a demonstration reactor to first commercial launch) ranged from 12 to16 years for these four commercial technologies. Key factors contributing to successful commercialization included durable government support for early R&D and varying degrees of public–private partnering through commercial launch. This partnering included arrangements for technical support, siting, facility ownership, nuclear material provision, and cost sharing. The policy environment was characterized by unambiguous government support; stabile, effective and informed government program management and oversight; and flexibility in the public–private partnership arrangements to promote technology development and demonstration. Government advocacy was structured to support progressively increasing industry independence and self-sufficiency. This experience is documented and analyzed in this paper to provide salient lessons and example program elements for contemporary efforts to stimulate development and commercialization of a new generation of advanced nuclear technologies through collaboration and public–private partnerships.

Analyzing daily sorties at NAS Kingsville utilizing alternative refueling methods

Training Air Wing TWO (TW-2) is responsible for the intermediate and advanced phases of Undergraduate Jet Pilot Training and provides the US Navy and Marine Corps with newly winged aviators. The objective of this study is to improve daily operations at TW-2 using sortie output and aircraft refuel waiting time as measures of performance. We define a sortie as any flight event attempted on the daily flight schedule to facilitate student training. We create a discrete-event simulation to model daily jet flow and operations at TW-2. We compare current operations with several alternative refueling strategies that incorporate hot pit refueling to minimize the time jets spend on the ground between consecutive sorties. We consider different factors and run a variety of experiments to determine the impact of various fueling strategies on resource utilization rates in an effort to generate more sorties. Our analysis finds that TW-2 can increase daily sortie throughput by an average of 60 sorties by incorporating both hot pit and cold refueling. We recommend using two hot pits to decrease the yearly idle fuel-burn cost by over $342,000 and reclaim a value of $98,422,500 from the annual maintenance contract at TW-2.

Simultaneous Damping and Frequency Control in AC Microgrid Using Coordinated Control Considering Time Delay and Noise

The incorporation of converter-based generating sources in utility-scale microgrids causes frequency instability and low-frequency oscillations (LFOs), which is also a reason for degeneration in system stability. Damping frequency control is an essential part of alternating current (AC) microgrid system operation and control. Sudden changes in load, variations in renewable power outputs due to changes in solar insolation or wind speed, and so on factors cause the system frequency to deviate from the nominal value. Therefore, the role of a frequency controller is to maintain the dynamic stability in an AC microgrid by retaining the system frequency at the nominal value. Again, AC microgrids with high renewable power penetration face even more difficulty in maintaining frequency stability because of their poor inertial response. The research presented here proposes a novel approach for grid-connected AC microgrid oscillation damping and frequency control that simultaneously takes into consideration time delay and noise. To improve the frequency response and dampen LFOs by providing the voltage and frequency within the specified range, a coordinated technique-based control approach is adopted. In the developed hybrid control, the frequency controller relies on active power modulation, while the power oscillation damping controller is dependent on reactive power modulation. To improve stability and reduce communication consequences such as noise and signal latency (time delay), the developed power oscillation damping controller and frequency controller are coordinated along with the robust linear quadratic Gaussian controller. The comparative investigation of the effectiveness of the coordinated control technique is employed in the software of MATLAB/Simulink for grid-connected AC microgrid. The outcome of the simulation illustrates the superior effectiveness of the suggested controller over the traditional droop controller for huge power flows under disturbance with various operating conditions, under/overfrequency events, time delay, and noise. This grid encouragement capability for AC microgrids is anticipated to lead to novel possibilities for generating revenue.

Effects of Ramp-Induced Incident Shock Impingement Under Various Slope Angle Configurations in a Strut-Based/Cavity-Assisted Supersonic Combustor

ABSTRACT The present study conducts a numerical investigation into the effects of ramp-induced shock impingement with different opening angles on the global flow-field and combustion behaviors within a strut-based/cavity-assisted scramjet combustor. To reproduce the intricate physical phenomena and all inflow properties of the primary channel, two-dimensional Reynolds-averaged Navier–Stokes (RANS) equations integrated with the shear stress transport (SST) k − ω turbulence model is implemented. The current approach considers several slope angle configurations of the ramp since they generate various shock systems that differ in magnitudes and coherent structures, substantially affecting the combustion efficiencies and chemical species distributions. Results reveal that an optimal shock angle of the ramp in correspondence with the current operating conditions can be achieved at 20°, which increases the combustion efficiency by 11% at the end of the cavity trailing edge without provoking excessive loss of total mass-flux-weighted pressure recovery. However, further expanding the slope angle above the optimum will trigger the flow blockage mechanism which enlarges the recirculation region on both sides of the ramp, simultaneously initiate their local thickening processes and create shock train areas owing to intensive interactions with the turbulent boundary layers. These phenomena are known for decreasing the total pressure ratio, adversely affecting all performance metrics of interest and destabilizing the main channel flow path.

Reverse‐Current Tolerance for Hydrogen Evolution Reaction Activity of Lead‐Decorated Nickel Catalysts in Zero‐Gap Alkaline Water Electrolysis Systems

AbstractAlkaline water electrolysis (AWE) systems offer a cost‐effective and scalable approach for large‐scale hydrogen production using renewable energy sources. However, their susceptibility to load fluctuations, particularly the reverse‐current (RC) phenomenon during shutdown events, poses a significant challenge to the long‐term stability and scalability of these systems. Herein, a catalytic approach for enhancing the RC tolerance in AWE systems by using Pb‐decorated Ni cathode catalysts (Pb/Ni) is introduced. The oxidation of Pb/Ni by repeated RC lowers the electromotive force for the reverse current operation, and consequently, imparts RC tolerance. Intriguingly, contrary to the expectation that the decoration with lead, an inert material for the hydrogen evolution reaction (HER), will interfere with the hydrogen generation of the Ni catalyst, the presence of Pb on the Ni cathode after the RC flow promotes both the proton desorption and water‐dissociation steps, improving the HER activity. Furthermore, the AWE stack testing with Pb/Ni catalysts is perfectly operated, demonstrating remarkably enhanced RC tolerance during startup/shut‐down (SU/SD) testing protocol. This paper presents a new strategy for mitigating the AWE performance degradation induced by RC flow and for achieving Pb/Ni catalysts with improved operational durability against RC flow in AWE systems.

Brackish groundwater desalination by constant current membrane capacitive deionization (MCDI): Results of a long-term field trial in Central Australia

A long-term field trial of membrane capacitive deionization (MCDI) was conducted in a remote community in the Northern Territory of Australia, with the aim of producing safe palatable drinking water from groundwater that contains high concentrations of salt and hardness ions and other contaminants. This trial lasted for 1.5 years, which, to our knowledge, is one of the longest reported studies of pilot-scale MCDI field trials. The 8-module MCDI pilot unit reduced salt concentration to below the Australian Drinking Water Guideline value of 600 mg/L total dissolved solids (TDS) concentration with a relatively high water recovery of 71.6 ± 8.7 %. During continuous constant current operation and electrode discharging at near zero volts, a rapid performance deterioration occurred that was primarily attributed to insufficient desorption of multivalent ions from the porous carbon electrodes. Performance could be temporarily recovered using chemical cleaning and modified operating procedures however these approaches could not fundamentally resolve the issue of insufficient electrode performance regeneration. Constant current discharging of the electrodes to a negative cell cut-off voltage was hence employed to enhance the stability and overall performance of the MCDI unit during the continuous operation. An increase in selectivity of monovalent ions over divalent ions was also attained by implementing negative voltage discharging. The energy consumption of an MCDI system with a capacity of 1000 m3/day was projected to be 0.40∼0.53 kWh/m3, which is comparable to the energy consumption of electrodialysis reversal (EDR) and brackish water reverse osmosis (BWRO) systems of the same capacity. The relatively low maintenance requirements of the MCDI system rendered it the most cost-efficient water treatment technology for deployment in remote locations. The LCOW of an MCDI system with a capacity of 1000 m3/day was projected to be AU$1.059/m3 and AU$1.146/m3 under two operational modes, respectively. Further investigation of particular water-energy trade-offs amongst MCDI performance metrics is required to facilitate broader application of this promising water treatment technology.

Eddy currents operation parameters optimization

With the development of non-destructive inspection techniques, more and more challenging situations have arisen and the correct choice of operating parameters can be decisive for a good detection sensitivity. Based on that, an algorithm was developed to obtain the best combination of an eddy current sensor operation parameters. The results obtained demonstrate the improvement in the cracks detection in welded parts after optimization.

Implementation of a low-cost intelligent street light system using internet of things

In the contemporary world, science and technology are advancing swiftly to meet the growing human need for electricity. Within this framework, street lighting, serving as the most vital and ubiquitous element of urban lighting infrastructure, contributes significantly to public electricity consumption. Hence, enhancing the operational efficiency of street lamps becomes imperative to conserve energy. Nonetheless, traditional street lighting systems, being manually controlled, consuming excessive power, and entailing high installation expenses, present notable drawbacks and concerns. Leveraging the internet of things (IoT), advanced innovations automate various areas, including health monitoring, traffic management, agricultural irrigation, street lights, and classrooms. The current manual operation of street lights leads to substantial global energy waste. To address this, an integrated hardware and software solution is proposed. Practical implementation of the hardware devices employs a wireless sensor network, while the software focuses on developing an IoT application for data storage, analysis, and visualization. The proposed system enables effective monitoring of parameters such as ambient temperature, current, voltage, and energy consumption of photovoltaic street lights, which are used as an indicator of the lamp status. Using Xbee modules, a configuration by the X-CTU software is necessary to communicate between all street lights wirelessly. These Xbee modules are used as a leading technology for wireless sensor networks due to its low power and low cost. Experimental results demonstrate that the proposed system is energy-efficient and cost-effective, and further can be implemented in real street light systems.

Nhận diện các công nghệ mới nổi ứng dụng trong các mô hình ngân hàng trên nền tảng số

The impact of the 4th industrial revolution, with the convergence of many technologies, has pushed banks to reshape their management and business models, aiming to build intelligent digital banks in the future. Emerging technology trends will reshape how financial institutions operate and interact with customers. Based on a comprehensive document review and analysis, the article identifies emerging technologies that strongly impact banking operating models, such as big data, artificial intelligence, blockchain technology, Internet of things, robotic process automation, hyperautomation, safety and security, and immersive technology. From there, the article analyzes the application of these technologies to current banking operating models such as open banking, banking of things, blockchain banking, and neobanking. In addition, the article also provides some discussions and recommendations for banks when applying emerging technologies to their current digital banking operating model

Evaluación estadística del circuito de clasificación-molienda de la planta de carbón en pulpa de Antapite de la compañía Sierra Sun Group (Perú) empleando modeol s estadísticos de regresión y ANOVA

El presente trabajo pudo mostrar una evaluación estadística del circuito de molienda – clasificación de una planta aurífera de la planta Antapite de la Compañía Minera Sierra Sun Group (Perú). El circuito de molienda empleaba un molino de bolas, cuyo mineral descargado tenía una la ley de oro de 29.50 g/t, el cual alimentaba a un hidrociclón convencional Krebs (de 10 pulgadas de tamaño, 254 mm), cuyo rebose tenía una distribución de oro de 71.30 % (para tamaños de partículas de menos 74 μm). Sin embargo, el objetivo fue aumentar esta distribución a un valor equivalente a 77.39% (para partículas menores a 74 μm), es decir, al menos 5 puntos por encima de la actual. Este rebose alimentó al circuito metalúrgico formado por concentración gravimétrica y/o cianuración – adsorción dependiendo de la mineralogía con la que se encontraba asociada el oro. Se realizó un análisis estadístico de regresión múltiple para las variables de operación del hidrociclón Krebs de 10 pulgadas (254 mm) que permitió obtener la distribución de oro (objetivo mencionado anteriormente). De esta manera se plantearon tres escenarios diferentes, en los cuales se eliminaron las variables no significativas. El primer escenario evaluó la dependencia del tamaño de corte corregido d50C en función de la presión y caudal de la pulpa de alimentación al hidrociclón, el segundo escenario analizó la variable independiente: distribución de oro en el rebose del hidrociclón con respecto a las variables dependientes: caudal alimentado, presión, d50 y d50C para tamaños de partículas menores a 74 μm. Por último, el tercer escenario permitió evaluar la dependencia de la distribución de oro objetivo frente al d50C. Finalmente, se logró definir el parámetro que influye en la dependencia de la distribución de oro objetivo en el rebose, pudiendo encontrar así el valor de d50 (tamaño de corte sin corregir en micras) en función del caudal y presión de trabajo para llegar a la distribución de oro objetivo de 77.39% en el rebose (para partículas menores a 74 μm).

Unsupervised Learning-Based Exploration of Urban Rail Transit Passenger Flow and Travel Pattern Mining

This study delves into the realm of urban rail transit systems, leveraging unsupervised learning techniques to analyze passenger flow characteristics and unearth travel patterns. Focused on the dynamic and complex nature of urban rail networks, the research utilizes extensive datasets, primarily derived from Automated Fare Collection (AFC) systems, to provide a comprehensive analysis of passenger behaviors and movement trends. Employing advanced algorithms like DBSCAN, the study categorizes passengers into distinct groups, including tourists, shoppers, thieves, commuters, and station staff. These classifications reveal intricate patterns in travel behaviors, significantly contributing to a deeper understanding of urban transit dynamics. The findings offer valuable insights into peak travel times, popular routes, and station congestion, highlighting potential areas for operational improvements and infrastructure development. The study’s application of unsupervised learning in analyzing vast, unstructured data sets a precedent in urban transportation research, showcasing the potential of artificial intelligence in enhancing the efficiency and sustainability of urban transit systems. The insights garnered are pivotal not only for optimizing current operations but also for shaping future expansion and adaptation strategies, ensuring urban rail systems continue to meet the evolving needs of growing urban populations.

Recognition of Ginger Seed Growth Stages Using a Two-Stage Deep Learning Approach

Monitoring the growth of ginger seed relies on human experts due to the lack of salient features for effective recognition. In this study, a region-based convolutional neural network (R-CNN) hybrid detector-classifier model is developed to address the natural variations in ginger sprouts, enabling automatic recognition into three growth stages. Out of 1,746 images containing 2,277 sprout instances, the model predictions revealed significant confusion between growth stages, aligning with the human perception in data annotation, as indicated by Cohen’s Kappa scores. The developed hybrid detector-classifier model achieved an 85.50% mean average precision (mAP) at 0.5 intersections over union (IoU), tested with 402 images containing 561 sprout instances, with an inference time of 0.383 seconds per image. The results confirm the potential of the hybrid model as an alternative to current manual operations. This study serves as a practical case, for extensions to other applications within plant phenotyping communities.

Re–operating the Bhumibol and Sirikit Dams Using Hybrid Neuro–Fuzzy Technique to Solve the Water Scarcity and Flooding Problems in the Chao Phraya River Basin

The decision support system to reservoir re–operation using Artificial Intelligence has been broadly studied and proven in term of the operational performances for both single and multiple reservoir system, this study applied Adaptive Neuro Fuzzy Inference System (ANFIS) technique for reservoir re–operation in Chao Phraya River Basin aiming to reduce water scarcity and flooding problems in the central region of Thailand. ANFIS is an integrated approach in which neural networks are utilized to enhance the fuzzy inference system and create fuzzy “IF–Then” reservoir operational guidelines with proper membership functions for reservoir re–operation. In this study, ANFIS operating rules were trained using two different datasets; long–term dataset (scenario 1) and water year–based dataset (scenario 2). It is revealed that the extent of yearly water deficit in critical dry years are totally reduced to nearly zero when re–operating with ANFIS operation rules, except in the year 2012. However, the yearly water deficit in year 2012 is also substantially reduced from 504 MCM by the current operation to 127 and 119 MCM for scenario 1 and scenario 2, respectively when two scenarios of ANFIS–based reservoir re–operation model were performed. Moreover, considerable total amount of spilled water from BB and SK Dams is definitely declined to 0 and 37 MCM in years 2002 and 2011, respectively when water year–based ANFIS model was implemented. In addition, it is expressed that average water storages of two main dams obtained from two scenarios of ANFIS model are substantially increased up to +6.08% and +6.94% for BB Dam and +0.09% and +1.62% for SK Dam in comparison with the current operation. This signifies that supplying water from dams to meet the target water demand through adaptive fuzzy–rules can be well handled and flooded water can be minimized.

Integrated system dynamics modeling and optimization for artisanal and small-scale gold supply chains

We address artisanal and small-scale gold mining in Peru. Using the data collected through field visits, we develop a System Dynamics (SD) model to examine the current operations of the gold supply chain, and its interplay with mercury and gas suppliers. The current mining operations cause potential health and environmental issues, due to high mercury usage, and other issues (e.g., safety issues). As a potential solution to overcome such problems, we consider incorporating a cyanide processing facility into the SD model, where we examine the amount of ore that a miner would be willing to sell to the facility under several payback scenarios. We formulate a mixed-integer programming model that prescribes the optimal transition plan of a miner from local production to selling their ore to the facility within a time horizon that maximizes a miner’s profit. The optimal solution provides a 22% improvement in the miners’ profit over that of their current operations. We also conduct a sensitivity analysis to test the sensitivity of the optimal solution to the changes in several input parameters. We discuss that the solution is most sensitive to the gold price changes and the least sensitive to the local production cost changes.

The Chilean Laja Lake: multi-objective analysis of conflicting water demands and the added value of optimization strategies

Abstract Water scarcity leads to conflicts over water allocation. Laja Lake in Chile is a natural lake, which was formed by a volcanic barrier. Outflow from the lake is created by seepage through the barrier and via a controllable artificial outlet, which adds reservoir characteristics to the lake. Hydroelectric power stations have been built at both outlets. Downstream, water is diverted into irrigation canals, and the Laja River forms the Laja Falls, a popular tourist attraction. The previous operating policy preferred the most upstream water user and was found to be inadequate because the lake level decreased over long term. The current reservoir operation policy was established through stakeholder negotiations. This study investigated whether optimization (using Non-Dominated Sorting Genetic Algorithm II) can further improve the operation of Laja Lake while maintaining a fair balance between stakeholder groups. The results were compared with the stakeholder agreement and the previous policy. The main difference is in the spring, when Laja Lake fills up before the irrigation season starts. The optimization strategy prioritizes hydropower generation during this period, resulting in reduced storage. Ultimately, optimization proves to be a valuable tool for identifying trade-offs and exploring different scenarios in water management.

Joint optimal operation of the South-to-North Water Diversion Project considering the evenness of water deficit

Abstract. Inter-basin water transfer projects are the main measure to address the water deficit crisis caused by uneven distribution of water resources. The current water transfer operation mainly tends to be present in areas with small water transfer costs and is prone to encounter the problem of spatial and temporal imbalances in water allocation. To address these issues, this paper defines a water deficit evenness index (WDEI), aimed at minimizing regional differences in water scarcity and sharing the pressure of water scarcity as a social demand objective. This index is incorporated into a joint optimization model for the Jiangsu section of the South-to-North Water Diversion Project (J-SNWDP), which comprises both the ecological objective of the total water deficit (TWD) and the economic objective of the pumping water (PW). Further, the Nondominated Sorting Genetic Algorithm III (NSGA-III) and multi-attribute decision-making were applied to solve the model and obtain an optimal operation strategy. The results showed the following: (1) the WDEI defined in this paper can mitigate the synchronized water scarcity in certain water users. In typical normal years (wet year and dry year), the WDEI shows a reduction of 94.2 % (81.8 %, 76.7 %) compared to the historical operation strategy. (2) The optimized operation strategy can significantly reduce TWD and PW by 82.06% (37.69 %, 52.36 %) and 45.13 % (3.25 %, 21.51 %) compared with the historical values, respectively, which can improve the water supply satisfaction and reduce the project cost. At the same time, the lake storage capacity of the optimal operation strategy performs well, and the water transfer efficiency of the river is significantly improved. (3) In this paper, targeted optimal operation strategies and potential ways to secure the project tasks are proposed for different natural flow. Overall, it is of great significance to study the water supply equity in the J-SNWDP to alleviate the concentrated water deficit in Jiangsu Province and other similar regions.

Development of Aqueous Organic Flow Battery Using SPEEK Membrane and Eco-Friendly Electrolytes

Aqueous organic redox flow batteries (AORFBs) are a type of flow battery that offers a promising solution for energy storage, and one of the main issues is selecting low-cost membranes with high ion conductivity to enhance performance and efficiency. In this study, low-cost sulfonated poly ether ether ketone (SPEEK) membranes were fabricated using the casting method, with the polymer/solvent ratios of SPEEK and N,N'-dimethylformamide (DMF) being varied. The Nafion 117 membrane was used as a benchmark for comparison. The performance of aqueous organic redox flow batteries employing anthraquinone-2-sulfonic acid (AQS) and 1,2-benzoquinone-3,5-disulfonic acid (BQDS) as electrolytes was evaluated. The SPEEK membranes were determined to have dense, homogeneous surfaces with no flaws and a thickness of 60 microns. In addition, their physicochemical properties, such as water uptake, swelling ratio, ion exchange capacity, and degree of sulfonation, were investigated. The results showed that the SPEEK membranes had better rate performance and cycle stability when compared to the Nafion117 membrane during charge-discharge cycles. Additionally, the SPEEK membranes exhibited slower potential drops and higher power density during constant current mode operation, despite showing no significant differences in energy efficiency and power density. These findings demonstrate the potential of SPEEK membranes for use in AORFBs and as a benchmark for future research and development.

Development of a Digital Twin Framework for a PV System to Resolve Partial Shading

Digital twin (DT) is a prolific buzzword in this era, where digitization plays a significant role. The perception of solar energy harvesting has been gaining popularity with the advent of solar panels. Solar asset maintenance is a need of the hour for investors because of the smart city scheme and green building certificate evaluation for all industries, educational institutions, etc. Among the list of factors that reduce PV system efficiency, the issue of partial shading is a vital distress that must be resolved. This paper focuses on the development of a digital twin framework that is proactively driven by shading patterns and a proposed optimization-based reconfiguration embedded controller that electronically relocates the panel in the physical world. The real-time system has been created for a three-by-three series parallel panel arrangement. The proposed switching matrix controller achieves the maximum power, i.e., 40% of increased power output, row current difference is made almost zero from 3 to 4 A, and fill factor increases by 20% by reconfiguring the solar array. It is done based on a decision taken by a nature-inspired (equal irradiation distribution algorithm) or puzzle-based (skyscraper) optimization algorithm. Switching matrix controllers overcome the disadvantages of physical relocation. The users can query the digital twin build to know the historical performance and current operating conditions of the system. It can trigger alarms as early warnings and make predictions about possible system anomalies, if and when they occur using a digital twin.