Simulation Tool Research Articles

Smart Learning and Climate Change Awareness: A Simulation‐Based Case Study in Morocco

ABSTRACTPrior research has highlighted the importance of smart learning in raising awareness and engagement about sustainable development. Nevertheless, few papers have focused on the impact of climate change simulation environments. In this paper, a systematic literature review has helped to shed the light on the research gaps and to propose a conceptual framework. Then, we have adopted a mixed method based on semi‐structured interviews and a questionnaire carried out for second year business students through different academic years. This case study aims both to compare the simulation‐based experience for two classes and to test our proposed framework's hypotheses through partial least‐squares method. The results have allowed us to explore the impact of using EN‐ROADS climate change simulator and to assess the relationship between climate change awareness, level of engagement and policy priorities. To the best of our knowledge, this is one of the first case studies exploring the potential impact of using climate change simulation tools. The study can help either practitioners like policy makers and university managers in adapting their curriculum, or researchers to test or extend our proposed framework.

Role and modulation of various spinal pathways for human upper limb control in different gravity conditions.

Humans can perform movements in various physical environments and positions (corresponding to different experienced gravity), requiring the interaction of the musculoskeletal system, the neural system and the external environment. The neural system is itself comprised of several interactive components, from the brain mainly conducting motor planning, to the spinal cord (SC) implementing its own motor control centres through sensory reflexes. Nevertheless, it remains unclear whether similar movements in various environmental dynamics necessitate adapting modulation at the brain level, correcting modulation at the spinal level, or both. Here, we addressed this question by focusing on upper limb motor control in various gravity conditions (magnitudes and directions) and using neuromusculoskeletal simulation tools. We integrated supraspinal sinusoidal commands with a modular SC model controlling a musculoskeletal model to reproduce various recorded arm trajectories (kinematics and EMGs) in different contexts. We first studied the role of various spinal pathways (such as stretch reflexes) in movement smoothness and robustness against perturbation. Then, we optimised the supraspinal sinusoidal commands without and with a fixed SC model including stretch reflexes to reproduce a target trajectory in various gravity conditions. Inversely, we fixed the supraspinal commands and optimised the spinal synaptic strengths in the different environments. In the first optimisation context, the presence of SC resulted in easier optimisation of the supraspinal commands (faster convergence, better performance). The main supraspinal commands modulation was found in the flexor sinusoid's amplitude, resp. frequency, to adapt to different gravity magnitudes, resp. directions. In the second optimisation context, the modulation of the spinal synaptic strengths also remarkably reproduced the target trajectory for the mild gravity changes. We highlighted that both strategies of modulation of the supraspinal commands or spinal stretch pathways can be used to control movements in different gravity environments. Our results thus support that the SC can assist gravity compensation.

Comparative Analysis of Geospatial Tools for Solar Simulation

ABSTRACTThis paper performs, describes, and evaluates a comparison of seven software tools (ArcGIS Pro, GRASS GIS, SAGA GIS, CitySim, Ladybug, SimStadt, and UMEP) to calculate solar irradiation. The analysis focuses on data requirements, software usability, and accuracy simulation output. The use case for the comparison is solar irradiation on building surfaces, in particular on roofs. The research involves collecting and preparing spatial and weather data. Two test areas—the Santana district in São Paulo, Brazil, and the Henio rural area in Raalte, the Netherlands—were selected. In both cases, the study area encompasses the vicinity of a weather station. Therefore, the meteorological data from these stations serve as ground truth for the validation of the simulation results. We create several models (raster and vector) to meet the diverse input requirements. We present our findings and discuss the output from the software tools from both quantitative and qualitative points of view. Vector‐based simulation models offer better results than raster‐based ones. However, they have more complex data requirements. Future research will focus on evaluating the quality of the simulation results on vertical and tilted surfaces as well as the calculation of direct and diffuse solar irradiation values for vector‐based methods.

Optimizing building performance with Digital Twins: Pathways to energy efficiency and decarbonization

In the context of current energy consumption and CO2 emissions, the impact of buildings is highly significant. In the quest for sustainable building management, the concept of digital twins emerges as a transformative technology, offering unprecedented capabilities for enhancing energy efficiency and reducing carbon footprints. This article explores the application of digital twins in the built environment, emphasizing their role in achieving significant energy and environmental benefits. Beginning with a brief discussion introducing the concept of digital twins, the article highlights their critical importance in modern building management and their potential to revolutionize sustainability practices. It then examines the role of IoT and AI in enabling advanced aspects of digital twin technology, including its technological functionalities. A comparative analysis of different simulation tools and methodologies follows, providing a comprehensive overview of how digital twins can be utilized to optimize building performance.

Investigation of cyclic water infiltration and dry-out in coated spruce using finite-element simulations

Accurate prediction of moisture distributions in wood is among the most critical challenges in timber engineering. Achieving this requires a well-coordinated comparison of experimental methods and simulation tools. While significant progress has been made in developing simulation tools in recent years, a lack of experience with and trust in these tools continues to hinder broader implementation, especially when it comes to free water and its absorption. Investigations and model advancements have allowed for the simulation of increasingly complex cases, including one-dimensional moisture transport above the fiber saturation point (FSP) in coated boards and below FSP in coated glued laminated timber (GLT). However, free water flow in coated GLT beams has not yet been addressed, which can become problematic in case of extreme scenarios, such as water infiltration. In this study, we demonstrate that the multi-Fickian free water transport model developed by some of the authors can successfully simulate three-dimensional coated cases. Uncoated and coated boards and GLT members were subjected to cyclic wetting and drying, both experimentally and numerically. To simplify the calibration process of the mass transfer coefficient of free water—identified as the most significant parameter for the simulation of free water transport—experiments previously conducted by some of the authors were simulated. Based on the simulation results, approaches for an initial estimation of the mass transfer coefficient were developed. If the water uptake of coated specimens is measured three days after continuous soaking in water and the result exceeds a specific limit, the coefficient can be sufficiently predicted. The simulation and experimental results show a good agreement.

Internal flow effect of a flexible riser system for a floating offshore wind turbine with on-board carbon dioxide capture

This paper designs a flexible riser for transporting carbon dioxide (CO2) off a floating offshore wind turbine (FOWT)-powered CO2 capture platform, and analyzes the internal flow-induced effects caused by the CO2 on the flexible riser. Internal effects on flexible risers due to the pressurized and internal dynamic flow are a well-studied problem in offshore oil and gas (O&G) applications, which typically requires the use of tools capable of representing their pressurized contents and flows. However, because the flow rates and pressure conditions expected from individual FOWT-CO2 capture platforms are much lower than those used in O&G installations, we studied the importance of internal flow effects on riser dynamics. To determine their relevance, we designed and modelled the flexible riser in OrcaFlex™ with different design pressure and flow conditions under normal and extreme environmental events. The results indicate that the riser’s effective tension and curvature are not significantly affected by internal flow effects, but differences were observed in the von Mises stress arising from the shear stress, which is a purely hydrostatic term. As such, as long as the shear term is properly accounted for, these results enable future work to utilize simplified models for the flexible riser system, similar to models for dynamic power cables employed in FOWT farms. This simplification allows us to design and analyze the whole FOWT-CO2 system alternatively with lower fidelity and open-source offshore wind turbine simulation tools, like OpenFAST, without overlooking relevant riser-dynamics phenomena.

I2Bot: an open-source tool for multi-modal and embodied simulation of insect navigation.

Achieving a comprehensive understanding of animal intelligence demands an integrative approach that acknowledges the interplay between an organism's brain, body and environment. Insects, despite their limited computational resources, demonstrate remarkable abilities in navigation. Existing computational models often fall short in faithfully replicating the morphology of real insects and their interactions with the environment, hindering validation and practical application in robotics. To address these gaps, we present I2Bot, a novel simulation tool based on the morphological characteristics of real insects. This tool empowers robotic models with dynamic sensory capabilities, realistic modelling of insect morphology, physical dynamics and sensory capacity. By integrating gait controllers and computational models into I2Bot, we have implemented classical embodied navigation behaviours and revealed some fundamental navigation principles. By open-sourcing I2Bot, we aim to accelerate the understanding of insect intelligence and foster advances in the development of autonomous robotic systems.

Research on Total Ionizing Dose Effect of double-trench SiC MOSFET

In this article, the impact of <sup>60</sup>Co-γ ray irradiation on double trench SiC metal–oxide–semiconductor field-effect transistors (MOSFETs) was investigated under different condiation. First, the effect of the total ionizing dose(TID) on the electrical performance of the device at different gate bias voltages was carried out. The results indicate that at 150 krad (Si) irradiation dose, the threshold voltage of the device after irradiation decreases by 3.28 V and 2.36 V for gate voltages of +5 V and -5 V bias, respectively, whereas the threshold voltage of the device after irradiation decreases by only 1.36 V for a gate voltage of 0 V bias. The threshold voltage of the device after irradiation drifts in the negative direction, and the degradation of the electrical performance is especially obvious under the positive gate bias. This is attributed to the increase in the number of charges trapped in the oxide layer. Meanwhile, room temperature annealing experiments were performed on the irradiated devices for 24, 48 and 168 hours. The shallow oxide trap charges generated by irradiation annealed at room temperature, while the deep oxide trap charges and interface trap charges were difficult to recover at room temperature, resulting in an increase in the threshold voltage of the devices after annealing, indicating that the electrical properties of the devices could be partially recovered after annealing at room temperature. In order to characterise the effect of <sup>60</sup>Co-γ ray irradiation on the interfacial state defect density of the devices, low frequency noise (1/<i>f</i>) tests were performed at different doses and different gate bias voltages. The 1/<i>f</i> low frequency noise test revealed that, under different bias voltages, the presence of oxide trap charges induced inside the oxide layer of the device after irradiation and the interfacial trap charges generated at the SiO<sub>2</sub>/SiC interface increased the irradiated defect density inside the device. This resulted in an increase of 4-9 orders of magnitude in the normalized power spectral density of the drain current noise of the irradiated device.To further obtain the irradiation damage mechanism of the device, a numerical simulation study was carried out using the TCAD simulation tool, and the results revealed that a large number of oxide trap charges induced by irradiation in the oxide layer cause an increase in the electric field strength in the gate oxide layer close to the trench side, which leads to a negative drift of the threshold voltage of the device and affects the performance of the device. The results of this paper can provide important theoretical references for the radiation effect mechanism and radiation reinforcement design of double trench SiC MOSFET devices.

Detectability of Self-lensing Flares of White Dwarfs with Compact Companions

Binaries containing compact objects, viewed nearly edge on, can produce periodic brightening events under certain conditions on the masses, radii, and binary separation. Such flares are caused by one object gravitationally lensing another, in what is known as self-lensing flares. We present a simulation tool that efficiently reproduces the main features of self-lensing flares and facilitates a detection sensitivity analysis for various sky surveys. We estimate the detection prospects for a handful of representative surveys when searching for systems of either two white dwarfs or a white dwarf with other compact objects, i.e., neutron stars and black holes. We find only a marginal ability to detect such systems in existing surveys. However, we estimate many such systems could be detectable by surveys in the near future, including the Vera Rubin Legacy Survey of Space and Time (LSST). We provide a quantitative analysis of the detectability of double-compact object self-lensing flares across the landscape of system parameters, and a qualitative discussion of survey and follow-up approaches to distinguish such flares from confounding events, such as stellar flares, satellite glints, and cosmic rays. We estimate 0.3, 3 and 247 double white dwarf systems could be detected by Transiting Exoplanet Survey Satellite, Zwicky Transient Facility, and LSST, respectively. A similar number of systems with a neutron star or black hole companion could be detected, but we caution that the number densities of such binaries is model dependent and so are our detection estimates. Such binaries can be used to constrain models of the end states of binary evolution.

Geant4-based technical simulation study of plastic scintillators for Positron Annihilation Lifetime Spectroscopy (PALS)

The influence of detector setup configuration and scintillator material choice on spectrum quality in Positron Annihilation Lifetime Spectroscopy (PALS) is fundamentally acknowledged primarily by empirical observation. However, this study quantifies the effects of ultra-fast plastic scintillators (BC422Q) within a conventional collinear (180-degree) detector setup used in the laboratory using the Geant4 simulation toolkit. We examine the impact of the scintillator's dimension and geometry (truncated cone vs. cylinder) on the detection efficiency for specific gamma-ray energies (1274 keV and 511 keV) and their proportion of corrupt events such as backscattering or multiple detections. Results indicate that the selection of scintillator dimension and shape significantly enhances detection efficiency albeit with an increase in corrupt events. Furthermore, we investigate the influence on the instrument response function (IRF) of the scintillators, showing how truncated cones offer superior precision and, thus, a narrower IRF compared to cylindrical shapes. Additionally, the effect of the sample material itself, in the case of a truncated cone as the scintillator shape, on the scintillator response was studied. It is observed that with an increasing atomic number of the sample the detection efficiency substantially decreases while the proportion of corrupt events also diminishes. Despite the sample material's influence on the energy of gamma-quanta interacting with the scintillator, no measurable impact on the IRF was detected for the chosen windows of the pulse height spectra. This investigation encourages a profound examination of the influences on spectrum quality in PALS measurements using Geant4 as a simulation tool highlighting the critical balance between detection efficiency and corrupt event frequencies.

Simulated near-field radioactive source localization in 3D with Coded Aperture and Convolutional Neural Networks

Coded Aperture γ-cameras have been extensively used in applications ranging from astrophysics to nuclear medicine for imaging radioactive source distributions. These devices allow the identification of the direction of γ-emitters by analyzing the shadow patterns projected onto pixelated detectors. In this work, we propose a novel approach based on Convolutional Neural Networks (CNNs) for accurately localizing radioactive sources in 3D using one coded aperture gamma camera. The CNN is trained using simulated shadowgrams generated by a custom simulation tool, with sources placed at various positions within the near-field, ranging from 20 cm to 120 cm from the detector. Unlike previous methods that focused on estimating angular coordinates, our model also accurately estimates the distance of the source, achieving a distance estimation accuracy of 10 mm, in addition to determining the polar and azimuthal angles. This capability is particularly relevant for medical imaging applications, where precise localization of radioactive sources is crucial. The results of our study demonstrate the potential of CNN-based algorithms in improving the accuracy and reliability of single-detector systems in near-field radioactive source localization.

Moving Toward Metaproteogenomics: A Computational Perspective on Analyzing Microbial Samples via Proteogenomics.

Microbial sample analysis has received growing attention within the last decade, driven by important findings in microbiome research and promising applications in the biotechnological field. Modern mass spectrometry-based methodology has been established in this context, providing sufficient sensitivity, resolution, dynamic range, and throughput to analyze the so-called metaproteome of complex microbial mixtures from clinical or environmental samples. While proteomic analyses were previously restricted to common model organisms, next-generation sequencing technologies nowadays allow for the rapid and cost-efficient characterization of whole metagenomes of microbial consortia and specific genomes from non-model organisms to which microbes contribute by significant amounts. This proteogenomic approach, meaning the combined application of genomic and proteomic methods, enables researchers to create a protein database that presents a tailored blueprint of the microbial sample under investigation. This contribution provides an overview of the computational challenges and opportunities in proteogenomics and metaproteomics as of January 2018. For practical application, we first showcase an integrative proteogenomic method that circumvents existing reference databases by creating sample-specific transcripts. The underlying algorithm uses a graph network approach that combines RNA-Seq and peptide information. As a second example, we provide a tutorial for a simulation tool that estimates the computational limits of detecting microbial non-model organisms. This method evaluates the potential influence of error-tolerant searches and proteogenomic approaches on databases of interest. Finally, we discuss recommendations for developing future strategies that may help overcome present limitations by combining the strengths of genome- and proteome-based methods and moving toward an integrated metaproteogenomics approach.

State‐of‐the‐art of cybersecurity in the power system: Simulation, detection, mitigation, and research gaps

AbstractIn a power system, the communication link can be compromised by intruders who can launch cyberattacks by capturing data packets, sending falsified packets, or stopping data packets from reaching their destination. Moreover, intruders can compromise control devices using supply chain attacks, firmware patching attacks, and insider attackers. Numerous cyberattacks have been reported previously, and cyberattacks are becoming more frequent since attackers are aware of their socioeconomic impacts. Extensive research has been conducted on developing platforms to simulate cyberattacks, studying different types of cyberattacks, investigating the adverse effects of a successful cyberattack on different components of the power system, designing ways to detect anomalies in the power system using electrical measurements, and proposing ways to mitigate the adverse effects of the detected cyberattack. This paper presents a review of state‐of‐the‐art of cybersecurity in the power system, reviewing available simulation tools for studying the cybersecurity of the power system, classifying components of the power system vulnerable to cyberattacks, and summarizing the adverse effects of a successful cyberattack on each component in the power system. Furthermore, different types of cyberattacks and detection and mitigation methods are classified. Research gaps in the cybersecurity of the power system are also discussed.

A wireless sensor network for coal mine safety powered by modified localization algorithm.

Coal mining industry is one of the main source for economy of every nations, whereas safety in the underground coal mining area is still doubtful. According to some reports, there is heavy loss of life and money due to the occasional accidents in the coal mining area. Some existing researchers has been addressed this issue and approached their method. But the limitations are the communication range between the anchor nodes are weak because of its complex infrastructure. In coal mines, gas leaks pose a serious safety risk, often leading to explosions or toxic exposure. Temperature control is difficult due to fluctuating underground conditions, potentially causing heat stress or equipment malfunction. Real-time worker tracking is challenging in these environments, making it harder to monitor safety and respond swiftly to emergencies. To overcome the challenges, the proposed research uses modified precise DV (Distance Vector)-Hop localization algorithm. The data is collected from the nodes and it will generate as anchor nodes and sensor nodes. The proposed research includes some parameters to analyze the abnormal environment characteristics such as network parameters, proposed localization algorithm parameters, simulation parameters, safety monitoring parameters. These parameters helps to send the safety alert to the ground center in case of any emergency such as high temperature, gas leakage, changes in air humidity. The proposed model has the ability to detect all the sensor nodes within the threshold value as well as outside the threshold value. This shows that the proposed model communication range and it accuracy is higher than the existing research. The MATLAB simulation tool is used to enhance the localization of the proposed algorithm where, it is compare the traditional DV-Hop localization algorithm. The proposed algorithm attain high performance accuracy than existing method. The efficiency of the proposed modified precise DV-Hop localization algorithm is evaluated by the simulation outcomes.

Project “BUSSARD” − a holistic development of high-efficiency solar cells covering innovative front-end, metallization and interconnection approaches

Within this work, we present key results of the transnational European research project “Bussard”. The aim of this project is the development and evaluation of various innovative approaches for highly efficient cell concepts such as tunnel oxide passivating contact (TOPCon) solar cells considering the whole process chain including front-end, back-end and module processing. We present atomic layer deposition (ALD) as a high-throughput alternative for the deposition of Al2O3 passivation layers on the front side of TOPCon solar cells enabling a substantial reduction of the emitter saturation current density down to j0e = 13 fA/cm2. In the field of metallization, we evaluate and demonstrate three innovative approaches for the fine-line metallization of TOPCon solar cells. In this study we focus on multi-nozzle parallel dispensing, a technology that was developed as an alternative to standard screen-printing metallization and is used for the metallization of TOPCon solar cells for the first time. By optimizing the fabrication process at Fraunhofer ISE, we realize TOPCon solar cells (156.75 mm × 156.75 mm) with a champion conversion efficiency of up to ηmax = 24.2% (independently confirmed by Fraunhofer ISE CalLab PVCells). Finally, we present a comprehensive evaluation of the innovative Tape Solution interconnection concept for TOPCon cells and modules. We demonstrate the feasibility on small-scale and full-format modules and analyze the I–V results as well as cell-to-module (CTM) loss analysis using the simulation tool SmartCalc®. The results are compared to TOPCon modules interconnected via SmartWire Connection Technology (SWCT) and electrically conductive adhesive (ECA).

Biogeochemical Modeling of High-Pressure/High-Temperature Bioreactor Systems for Enhanced Microbial Risk Assessment in Underground Hydrogen Storage

Summary Underground hydrogen storage (UHS) can be a valuable solution for efficient and environmentally friendly energy storage but it introduces complex microbial and geochemical interactions that pose unique challenges. This research leverages advanced biogeochemical modeling to accurately replicate these interactions, reproduced within a laboratory-scale bioreactor system that mimics the high-pressure and high-temperature conditions typical of many underground gas storages. Utilizing a dual-platform approach, we used COMSOL® Multiphysics and CMG-GEM, augmented by supplementary simulation tools like PHREEQC, to perform an in-depth analysis of the evolution of microbial populations and gas and liquid composition, and of the hydrochemical processes in geological formations. Our comparative study demonstrates the effective application of these platforms in modeling the complex dynamics of heat and fluid dynamics, mass transfer, and biochemical reactions. The models were meticulously validated against experimental data, displaying high accuracy in kinetic parameter fitting and the ability to replicate the observed phenomena, including microbial hydrogen consumption rates below 0.05% under specified conditions and no detectable H₂S production at high pressures. The simulation results from COMSOL® and CMG-GEM showed remarkable agreement, with differences in the respective outcomes under 3–5%, confirming the reliability and robustness of the simulations across different computational environments. The research highlights the benefits of integrating multiple simulation platforms to achieve a comprehensive and comparative understanding of biogeochemical processes at various scales. This approach not only enhances our predictive capabilities but also facilitates the transfer of biochemical and geochemical kinetics from bioreactor-scale to reservoir-scale models, to make the implementation of hydrogen storage possible. These findings underscore the potential of the modeling tools to support the assessment and management of microbial risks associated with hydrogen storage, contributing to fully assessing the storage feasibility. By providing a detailed comparison of two leading software platforms, we established an essential methodological framework for advancing the UHS technology toward safe implementation.

CellSpark: A Simulation Tool to Spark Discovery Learning of Electrophysiology

ChallengeWhile hands-on laboratory experiences are an important component of biomedical engineering curricula, many of the topics which would often benefit most from these experiences are often too time-, space-, and resource-intensive to incorporate into laboratory classes. One such topic is electrophysiology, where phenomena are hard to observe and the mathematical models used to describe them are complex.Novel InitiativeWe present CellSpark, an electrophysiology simulation software that allows students to perform virtual patch clamp experiments without the need for complex laboratory equipment, training, or prior knowledge in mathematical modelling. We describe how this tool is implemented in an undergraduate bioinstrumentation lab through the theoretical framework of discovery learning.ReflectionThe use of the software tool was informally assessed by surveying students about their experience using the software, by analyzing the content of their submitted assignments for evidence that the learning objectives were met. Additionally, an individual use case is presented to demonstrate the utility of the tool to achieve authentic discovery learning. Overall, we successfully demonstrate a framework for the effective use of simulation tools designed primarily for research as teaching tools.

Judgmental Forecasting Uses Agent-Based Modeling and Simulation to Minimize Risks and Losses in Decision-Making

Forecasting is widely used in many aspects of human life whether it is informal for personal use or formal in organizations or institutions. This study delves into the utilization of judgmental forecasting in determining the ideal number of orders to restock inventory to supply to the restaurant. Through modeling and simulation, this research aims to mitigate uncertainties, reduce risks, and prevent losses. By quantifying the owner's mood, this study comprehensively analyzes the total cost of calculations based on decision-making theories and the mood of the decision-maker. The research employs the NetLogo simulation tool, which is commonly utilized in creating agent-based models and simulations. After conducting five simulations with 1000 data points each, it was discovered that relying on mood for decision-making resulted in a higher total cost ranging from 0.44% to 45% compared to the theoretically calculated cost. Mood-based decision-making is generally riskier and incurs cost losses.

Optimization of the finite element meshing for the fabrication of a M3x12x0.5 screw, in 3D printing

Meshing optimization in the finite element method (FEM) is key to designing mechanical components such as screws. This study focuses on the M3x12x0.5 screw, improving its performance and durability through the appropriate selection of meshing parameters. FEM software was used to analyze its behavior under different loads, comparing it with a 3D printed model. The results showed improvements in the accuracy of predictions about resistance, deformation and stress distribution, allowing the design to be optimized. This highlights the relevance of simulation tools in the development of mechanical components.

Fuzzy logic-PLC-based controller for water treatment simulator system

The constant demand for clean water is critical for both consumption and daily activities. A water pH control system is essential for regulating and determining the concentration of acid and base values in water. Research projects often employ a Programmable Logic Controller (PLC) control system with various control methods. This serves as the backdrop for the design of a water treatment simulator, which utilizes a fuzzy logic control system. Fuzzy logic control is a reliable method that produces effective and accurate output values. Experiments conducted on the designed water treatment simulator demonstrate this. Factors influencing the relationship between the solution pump response time and the normalized water pH results are the concentrations of the pH-UP and pH-DOWN solutions. The water treatment simulator is a lab-scale water pH control system simulation tool using a fuzzy logic control mode. It uses an Outseal PLC microcontroller, a pH sensor to measure pH, a DC pump, and a solenoid valve to control the output flow. The experimental results show a significant relationship between the initial pH value of the water measured and the response time of the solution pump. Data analysis shows a positive correlation where the further the pH concentration value of the water is from the normal pH state, the higher the response time value. Factors influencing the relationship between the solution pump response time and the normalized water pH results are the concentration of the pH UP and pH DOWN solutions. The concentration of the solution is set for a pH UP of 12.2 and a pH DOWN solution of 2.2.