Environmental Operating Conditions Research Articles

Floating offshore wind turbine nonlinear model predictive control optimisation method

This paper presents a novel control parameter optimisation methodology for nonlinear model predictive control for floating offshore wind turbine operation, computing optimisation weights as environment conditions dependent variables. The main objective is to reduce the required time to define the optimal control parameters for the nonlinear control strategy, using an automated approach. To achieve this, an optimisation methodology based on extreme operational gust conditions is applied by employing a Random Walk-type Monte Carlo procedure. The primary aim is to introduce an advanced control design approach that addresses concerns related to the efficient power generation and longevity of floating systems, particularly considering the growing scale of wind turbines and the dynamic behaviour of floating platforms, which increase the system overall costs. The resulting optimised controller is also evaluated against state-of-the-art feedback-based control strategies in different operational environmental conditions.

ANALYSIS OF THE IMPACT OF CROSS-SECTION DAMAGE ON THE STRENGTH AND DEFORMABILITY OF BENT REINFORCED CONCRETE ELEMENTS

The article analyzes defects and damage in reinforced concrete structures, particularly physical, biological, and chemical, with an emphasis on the impact of prolonged operation and aggressive environmental conditions. Research shows that mechanical damage, such as spalling and potholes, significantly reduces the load-bearing capacity of structures and causes complex deformations. Relevant directions in scientific research have been identified, particularly regarding the behavior of damaged reinforced concrete beams under load, which require further development and improvement of methods for assessing residual load-bearing capacity. The article emphasizes the need for additional experimental studies and the use of modern software for more accurate methods of predicting and calculating reinforced concrete structures.

A Data-Driven Model for Rapid CII Prediction

The shipping industry plays a crucial role in global trade, but it also contributes significantly to environmental pollution, particularly in regard to carbon emissions. The Carbon Intensity Indicator (CII) was introduced with the objective of reducing emissions in the shipping sector. The lack of familiarity with the carbon performance is a common issue among vessel operator. To address this aspect, the development of methods that can accurately predict the CII for ships is of paramount importance. This paper presents a novel and simplified approach to predicting the CII for ships, which makes use of data-driven modelling techniques. The proposed method considers a restricted set of parameters, including operational data (draft and speed) and environmental conditions, such as wind speed and direction, to provide an accurate prediction of the CII factor. This approach extends the state of research by applying Deep Neural Networks (DNNs) to provide an accurate CII prediction with a deviation of less than 6% over a considered time frame consisting of different operating states (cruising and maneuvering mode). The result is achieved by using a limited amount of training data, which enables ship owners to obtain a rapid estimation of their yearly rating prior to receiving the annual CII evaluation.

Heat transfer study on a stator-permanent magnet electric motor: A hybrid estimation model for real-time temperature monitoring and predictive maintenance

When electrical machines operate without a specific cooling system, the surrounding environment plays a crucial role in the rise of temperature and the duty cycle of operation. More clearly, a natural convection cooling system with a low value of heat transfer coefficient carries the risk of thermal breakdown, insufficient safety, and reliability. This paper studies the heat transfer aspects of a low-power flux switching permanent magnet (FSPM) motor under natural convection cooling to implement a novel real-time sensor-less temperature monitoring system. Thermal and electromagnetic experiments are carried out to create foundations for transient and steady-state numerical models. A data-driven, deep learning algorithm estimates the core and permanent magnet (PM) eddy current losses in real-time, besides the already available copper and friction losses. Subsequently, a two-node thermal equivalent circuit in a hybrid model with a feed-forward neural network estimates the dynamic temperature profile of windings and PMs. It is indicated that the worst-case estimation error is below 7.5%, and the configuration is applicable under a wide range of operation states and environmental conditions. Lastly, the system, including the power source, FSPM motor, and hybrid temperature estimation unit, will be implemented in MATLAB/Simulink to investigate the fault prediction and operation management capabilities.

Unraveling the impact of operational parameters and environmental conditions on the quality of viable bacterial aerosols.

Viable pathogen-laden droplets of consistent quality are essential for reliably assessing the protection offered by facemasks against airborne infections. We identified a significant gap in guidance within standardized tests for evaluating the filtration efficiencies of facemask materials using viable bacteria-laden aerosol droplets. An aerosol platform, built according to the American Society for Testing and Materials standard F2101-19, was used to validate and standardize facemask filtration test procedures. We utilized this platform to investigate the impact of varying five operating parameters, namely suspension media composition, relative humidity, pathogen concentration, and atomizer airflow and feed flow rates, on the aerosol quality of viable bacteria-laden aerosols. We achieved consistent generation of 1,700 to 3,000 viable bacteria-laden droplets sized between 2.7 and 3.3 µm under the following optimized test conditions: 1.5% w/v peptone water concentration, ≥80% relative humidity at 24 ± 2 °C, 1 × 105 CFU/mL bacterial concentration, 1.5 L/min atomizer airflow rate, and 170 μL/min feed flow rate. We also explored the consequence of deviating from these optimized test parameters on viable bacteria-laden aerosol quality. These results highlight the importance of controlling these parameters when studying airborne transmission and control.

A-338 Assessing the Technical and Analytical Performance of Point of Care Testing Devices for Blood Lipids Measurements

Abstract Background Cardiovascular diseases (CVDs) are the leading cause of death worldwide, causing over 17 million deaths each year. According to the World Health Organization (WHO), 85% of CVD deaths are due to heart disease and strokes, and over 75% occur in low- and middle-income countries (LMICs). Early detection and treatment are key to preventing consequences of CVD. Point-of-care testing (POCT) devices require minimal laboratory infrastructure and can be used to screen hard-to-reach populations for CVD risk. These devices are increasingly used in traditional care settings including hospitals and primary healthcare, and in non-traditional settings such as pharmacies. Although POCT devices offer fast and convenient testing, the analytical performance of these devices must produce accurate and reliable results, and have technical suitability for use. Several POCT devices for total cholesterol (TC) and other blood lipids are commercially available, however, the operational and analytical performance of these devices is not fully known. Methods Eight POCT devices for blood lipids were used in this study. For the technical assessment, a checklist was developed and applied, outlining the operational functionality and environmental conditions of the devices. Some relevant technical parameters in this checklist included requirements for temperature/humidity and power/voltage, storage conditions, and assay/test time. The analytical performance was assessed for parameters, such as accuracy and imprecision, using paired whole blood and serum donor samples for TC. In addition, these analytical parameters were assessed for serum High Density Lipoprotein-cholesterol (HDL-c) and Low Density Lipoprotein-cholesterol (LDL-c) samples on seven POCT devices. All samples were representative of a clinically relevant concentration range for TC, HDL-c, and LDL-c. Accuracy was compared to reference values obtained by the CDC reference measurement procedures. Results The mean bias for TC ranged among devices from -8.17% to 11.99% for whole blood and -7.89% to 7.02% for serum, respectively. The mean imprecision for TC in whole blood ranged from 1.94% to 11.63%, and in serum from 1.72% to 12.70%. The mean bias for serum HDL-c samples ranged from -7.33% to 17.36%, and for serum LDL-c samples from -20.92% to 5.04%. The mean imprecision for serum HDL-c samples ranged from 2.39% to 24.27% and for serum LDL-c samples ranged from 3.14% to 18.33%. For three of the eight devices, about 25% of the whole blood measurements and 50% of the serum measurements were reported outside of the measurement range and not included in the assessment. Comparing these non-reports with the reference values of these samples indicate that all of the reference values for whole blood and about 80% of the reference values for serum were actually within the measurement range of the device. Conclusions The technical assessment was helpful in building screening capacity in areas with limited laboratory infrastructure. The analytical assessment indicates notable differences in accuracy and precision for lipids measured in whole blood and serum, with serum measurements being more likely within performance requirements used by CDC's standardization programs. Additional studies to assess other devices and/or analytes, and future work to assist manufacturers improve the analytical performance of POCT devices are needed.

Study on the diffusion and blocking behavior of bubble plumes in Cross-Flow

Abstract With the deepening of the concept of environmental protection, environmental problems in dredging engineering have attracted more and more attention. The diffusion of suspended particulate matter is inevitable in the process of dredging. A bubble plume, as a multiphase linear plume, has been widely studied because it can block the diffusion of suspended particulate matter in water. To explore the formation characteristics of the bubble plume, we evaluate the formation quality of the bubble plume qualitatively and quantitatively, analyze the effect of the bubble plume on blocking the diffusion of suspended particulates under different operating conditions (the type of bubble plume generation device, the number of bubble plume rows, and the ventilation flow rate) and environmental conditions (water flow velocity), and study the movement trajectory of blocked particles and blocked failed particles. A series of experiments on bubble plume characteristics and bubble plume blocking the diffusion of suspended particles were conducted to analyze the particle distribution characteristics and bubble plume blocking effect under the interaction of transverse flow and bubble plume. The results show that the better the quality of the bubble plume is, the lower the transverse flow intensity is, and the better the barrier effect of the bubble plume is.

Airline business models as complex systems: assessing component interdependencies through interpretive structural modelling

PurposeThis paper explores the intricate interdependencies among core components of airline business models (BMs). In the airline industry, where BMs are complex systems, a successful BM requires an orchestrated configuration of various components. However, there is a paucity of research in BM literature pertaining to the interrelationships among key components of airline BMs.Design/methodology/approachEmploying interpretive structural modelling, we gathered input from experts in Iran to assess the driving power and dependency of elements within airline BMs.FindingsOur findings highlight the significance of operating environment conditions and competitive market dynamics as pivotal external components shaping the foundational structure. Value proposition, customer relationship management, and process monitoring are crucial linkage components that drive power and dependency. Notably, capturing value is positioned with the highest dependency.Practical implicationsWe utilised the ISM technique to visualize interdependencies within airline business models, aiding strategic decision-making. Our findings suggest aligning business and operational strategies with market needs ensures effective value creation and capture, maintaining competitive advantage in the airline industry. In addition, our research reveals critical factors affecting value creation and capture, emphasising monitoring the operating environment and competitive market, and strategically managing value propositions and customer relationship initiatives in the airline industry. We advise adapting business models to external changes for sustained growth and recommend regular monitoring of industry trends and customer expectations.Originality/valueFramed within complexity theory, these insights offer valuable perspectives on identifying and situating critical BM components in the airline industry. The practical implications derived from this study serve as strategic tools for airline managers and potential investors to optimise the design of their airline BMs.

Investigation of operational settings, environmental conditions, and faults on the gas turbine performance

Abstract Gas turbine engines are complex mechanical marvels widely employed in diverse applications such as marine vessels, aircraft, power generation, and pumping facilities. However, their intricate nature renders them susceptible to numerous operational faults, significantly compromising their performance and leading to excessive emissions, consequently incurring stringent penalties from environmental regulatory bodies. Moreover, the deterioration of gas turbine performance is exacerbated by variations in working conditions based on operational settings and environmental conditions. Past studies have focused on certain working conditions that limit effectiveness in real-world applications where operational settings and environmental conditions vary during operations. The influence of these working conditions on the performance of gas turbines also needs to be assessed, as they can lead to different fault patterns resulting in unplanned maintenance, unnecessary maintenance costs, unsafe conditions and stringent penalties. This study uses the gas turbine simulation program to simulate a high-bypass turbofan engine inspired by Pratt & Whitney PW-4056, analysing the combined effects of operational settings and environmental conditions on engine performance while also incorporating simulations of common gas turbine faults like fouling and erosion in various locations and severities along the gas path. The model’s accuracy is confirmed by low mean absolute percentage errors of 0.004% of thrust at the cycle reference point and 0.15% and 0.28% at 2 km and 7 km altitudes, respectively, demonstrating the model’s robustness across varying operational scenarios. In conclusion, this research highlights the significant effects of operational settings and environmental factors on gas turbine performance, particularly impacting specific fuel consumption and thrust. The study reveals that operational settings and environmental factors significantly impact fuel consumption and thrust. Specifically, compressor fouling and low-pressure turbine erosion increase nitrogen oxide (NOx) emissions by 4.5% and 11.1%, while fouling of nozzle guide vanes and high-pressure turbine erosion raise unburned hydrocarbon by 10.0% and 20.2%, and carbon monoxide (CO) by 3.2% and 5.2%, respectively, compared to a healthy engine. These insights highlight the importance of component-specific degradation in influencing gas turbine performance and emissions.

Electro-activation for efficient lactulose production: The impact of operational parameters, electrolyte types, and environmental conditions

The electro-activation (EA) process has been employed to convert lactose into lactulose, a potent prebiotic with numerous health benefits. The study identified optimal conditions for lactulose production, including a specific current density, the use of calcium chloride as an efficient electrolyte, and the superiority of a steel cathode over a graphite cathode. The formation of intermediates other than lactulose increased as the temperature increased. The changes in pH and oxidation-reduction potential during the isomerization process were observed, highlighting their vital role in the formation of an ideal alkaline environment for lactulose production. The resultant solution produced by the EA process has significant antioxidant characteristics, likely due to the formation of Maillard Reaction Products during lactulose production. In conclusion, the EA system, with optimal operating conditions, is an energy-efficient, reagent-free, and environmentally friendly alternative for lactulose production.

From single to multi‐purpose reservoir: A framework for optimizing reservoir efficiency

AbstractThe prevailing challenge centers on the limited application of single‐purpose flood control reservoirs transitioning to multi‐purpose reservoirs, which, despite their potential, often fall short in addressing the escalating demands for diverse water resource management. These challenges are compounded by outdated operational rules and changing environmental conditions. This research develops a framework to enhance the dual functionality of reservoirs, initially designed for flood control, to also support water supply through the determination of maximum safe water levels (MSWLs). Utilizing historical inflow data and reservoir simulation models, the study identifies opportunities for optimizing United States Army Corps of Engineers Louisville District reservoirs. It highlights certain reservoirs as ideal for augmenting water supply capabilities without compromising flood control performance. Others remain critical for flood management due to limited water supply potential, underscoring the importance of maintaining a focus on flood control. The findings illuminate the intricate balance required between managing flood risks and enhancing water supply, indicating that precise operational adjustments can significantly improve reservoir sustainability and efficiency. This method offers a viable pathway to convert single purpose reservoirs into multi‐purpose reservoirs, meeting growing water demands while ensuring robust flood mitigation, and making a step toward better water utilization.

A study of XLPE insulation failure in power cables under electromagnetic stress

Underground cables with cross-linked polyethylene (XLPE) insulation are integral to medium voltage (MV) power transmission systems, ensuring continuous electricity supply amidst operational challenges and environmental conditions. However, the reliability of these cables can be compromised over time due to aging and installation-related factors, particularly at joints and terminations. This study offers a comprehensive analysis of how various defects, including spherical air voids, pinholes, and irregularities in semiconducting layers, affect electric field and potential distributions within cable end terminations using COMSOL Multiphysics software. Through detailed simulations, the study identifies significant variations in electric field strength caused by these defects, highlighting critical stress concentration areas. In this study, it assumed that the cable and termination have the same cross-section. By analyzing these simulations, the study provides insights into optimizing cable design and installation practices to enhance the reliability and lifespan of underground power transmission systems. This study introduces a novel approach by combining advanced COMSOL Multiphysics simulations with a detailed analysis of defect impacts on electric field distributions, offering new insights into stress concentrations and degradation at cable terminations. Simulation outcomes reveal significant variations in electric field strengths due to air voids and pinholes in cables and terminations: for 2 mm voids, up to 2.45×106 V mm−1 near the conductor and 56.5 V mm−1 at termination. These findings enhance the understanding of XLPE-insulated cable behavior under electromagnetic stress, providing a basis for mitigating failures and improving overall system performance.

Optimizing water resources for sustainable desalination: The integration of expert systems and solar energy in experimental applications

The integration of renewable energy sources with multi-energy systems present challenges and opportunities to enhance sustainability. Among these, solar stills have emerged as a solution for water desalination. With the advent of expert system technologies, avenues are opened for improving the operational efficiency of solar distillers. This paper presents an innovative approach utilizing correlation analysis, ReliefF for feature selection, and a k-Nearest Neighbor (kNN) algorithm for forecasting the cumulative distillate output of a double slope solar still. The analysis is based on a 6-cases-based dataset, which includes variations in distillate output relative to different operational-environmental conditions. Key features that significantly impact overall performance were identified to manage the solar distiller productivity. The findings reveal that the maximum distillate output was 1610 ML/m2.day due to incorporating reflective materials and phase change materials (PCM) in enhancing distillation rates. The kNN model was evaluated based on its R2, RMSE, and CVRMSE, with the best models achieving scores of 0.995, 0.0033, and 0.1666, respectively. These metrics underscore the effectiveness of the proposed machine learning approach in predicting distillate output, thereby enabling informed management of solar distillation processes. Combining renewable energy technologies and computational intelligence holds significant promise for sustainable environmental management, as the study presented.

Unsupervised Transfer Learning Method via Cycle-Flow Adversarial Networks for Transient Fault Detection under Various Operation Conditions.

The efficient fault detection (FD) of traction control systems (TCSs) is crucial for ensuring the safe operation of high-speed trains. Transient faults (TFs) can arise due to prolonged operation and harsh environmental conditions, often being masked by background noise, particularly during dynamic operating conditions. Moreover, acquiring a sufficient number of samples across the entire scenario presents a challenging task, resulting in imbalanced data for FD. To address these limitations, an unsupervised transfer learning (TL) method via federated Cycle-Flow adversarial networks (CFANs) is proposed to effectively detect TFs under various operating conditions. Firstly, a CFAN is specifically designed for extracting latent features and reconstructing data in the source domain. Subsequently, a transfer learning framework employing federated CFANs collectively adjusts the modified knowledge resulting from domain alterations. Finally, the designed federated CFANs execute transient FD by constructing residuals in the target domain. The efficacy of the proposed methodology is demonstrated through comparative experiments.

Research on Sea Trial Techniques for Motion Responses of HDPE Floating Rafts Used in Aquaculture

The innovative aquaculture equipment known as high-density polyethylene (HDPE) floating rafts has gained popularity among fishermen in the southeast coastal regions of China. Compared to deep-water anti-wave fish cages, the construction costs of HDPE floating rafts are 50% to 75% less. There is a dearth of comprehensive publicly available records of HDPE floating rafts sea trial data, despite substantial numerical studies on the motion response of aquaculture fish cages and scale model experiments under controlled-wave conditions. This study involves sea trial techniques under operational and extreme environmental conditions for motion responses of HDPE floating rafts, presents a comprehensive procedure for sea trials of HDPE floating rafts, summarizes the issues encountered during the trials, and suggests solutions. Using MATLAB for independent programming, motion videos and photos collected from the sea trials are processed for image capture, yielding the original time history curve of vertical displacement. Based on the sea trials’ data, including motion displacement, acceleration, mooring line force, overall deformation patterns, and current and wave data, recommendations are provided for the design and layout of HDPE floating rafts. Based on the Fast Fourier Transform (FFT) method for spectral analysis, the influence of interference items on the observational data is eliminated; the rationality of the observational data is verified in conjunction with the results of the Gabor Transform. This study offers a scientific analytical method for the structural design and safe operation of HDPE floating rafts and provides a reference for subsequent numerical simulations.

A Comparative Analysis of Support Vector Machine and Decision Tree Algorithm for Predicting Fault in Uninterruptible Power Supply Systems

Power supply systems can have problems, and Ghana Gas Limited is not an exception. Ghana Gas Limited uses an intricate Uninterruptible Power Supply (UPS) system which is made up of several parts such as electromechanical components, PCB boards, and electrolytic capacitors. The majority of components have technical lifespans that are governed by usage, operational environment, and working conditions, such as electrical stress, working hours, and working cycles. Most of the time, these errors affect the integrity and power supply after manufacture. The issue is that it takes longer for the professionals who operate on this machine to recognize these flaws, which makes it difficult for them to predict errors quickly or anticipate the likelihood of faults happening in the system components at an early stage for effective corrective action to be performed. Support vector machines (SVM) and decision trees were used in this study to anticipate faults for technical data scheduling of uninterruptible power supply systems for Ghana Gas Limited in an efficient manner. Based on a comparative analysis using these two techniques, faults in Ghana Gas Limited's power supply system were predicted using a four-hour daily interval dataset on UPS recordings, including input voltage, battery voltage, battery current, and alarm, spanning from August 2017 to October 2023. The findings depicted that the support vector machine was more efficient in detecting the fault locations in the power supply system with an accuracy of 96.80%, recall of 99.80%, precision of 100 %, F1-score of 93.15%. The results from the error metrics also validate the measures in assessing the predictive ability of the model with MAE of 0.42%, MSE of 1.18%, RMSE of 4.45%, R2 of 99.97%, RMSLE of 0.036%, and MAPE of 0.21%.

Research on structural damage identification method of aircraft aluminium alloy skin based on power spectral entropy

Skin is an important structural component which covers the surface of aircraft widely, the structural health of skin has a great impact on the overall safety of aircraft. Due to the harsh operating environment, complex and changing working conditions, and high-intensity service process of aircraft, it is easy to cause damage to the skin. Thus, the in-situ health monitoring for aircraft skin is an effective approach to ensure its safety and reliability. Thanks to the ability of Lamb waves to propagate over long distances and recognize small-scale defects, they have shown great potential in the health monitoring of thin-walled structures and have received widespread attention. However, the traditional Lamb wave-based damage identification methods generally suffer from the problems of large number of sensors and low efficiency. Therefore, a skin damage identification method based on power spectral entropy is proposed in this paper, which can estimate the damage extent by extracting the nonlinear component increase caused by damage, and the experiments validate that the proposed method has higher sensitivity and computational efficiency than the existing other entropy-based methods.

Seamless Switching Control Strategy for a Power Conversion System in a Microgrid Based on Extended State Observer and Super-Twisting Algorithm

Microgrids can operate stably in both islanded and grid-connected modes, and the transition between these modes enhances system reliability and flexibility, enabling microgrids to adapt to diverse operational requirements and environmental conditions. The switching process, however, may introduce transient voltage and frequency fluctuations, causing voltage and current shocks to the grid and potentially damaging devices and systems connected to the microgrid. To address this issue, this study introduces a novel approach based on the Extended State Observer (ESO) and the Super-Twisting Algorithm (STA). Power conversion systems use Virtual Synchronous Generator (VSG) control and Power-Quality (PQ) control when they are connected to the grid or when the microgrid is not connected to the grid. VSG and PQ share a current loop. Transitioning the reference current generated by the outer loop achieves the switching of control strategies. A real-time observer is designed to estimate and compensate for current fluctuations, disturbances, and variations in id, iq, and system parameters during the switching process to facilitate a smooth transition of control strategies. Furthermore, to enhance the dynamic response and robustness of the system, the Proportional–Integral (PI) controller in the ESO is replaced with a novel super-twisting sliding mode controller based on a boundary layer. The Lyapunov stability principle is applied to ensure asymptotic stability under disturbances. The proposed control strategy is validated through simulation using a seamless switching model of the power conversion system developed on the Matlab/Simulink (R2021b) platform. Simulation results demonstrate that the optimized control strategy enables smooth microgrid transitions, thereby improving the overall reliability of grid operations.

Examining differences in the uptake of corrosive gases in polymer films and its dependence on temperature and relative humidity using a novel procedure combining sample weathering and LA-ICP-MS analysis

In polymer coating applications for technological products exposed to harsh environmental operating conditions, it is essential to provide and extend the protection capability of the coating material against corrosive gases, such as hydrogen sulfide or sulfur dioxide. Therefore, applied materials are subject to ongoing developments to improve their properties. Such developments aiming to establish improved polymer materials can only be performed accompanied by suitable testing procedures and analysis methods, for which a versatile approach is proposed in the presented work.We developed a testing approach, combining a mixed flowing gas setup (MFG) that allows flexibly adjustable exposure conditions for weathering experiments, and laser ablation – inductively coupled plasma – mass spectrometry (LA-ICP-MS) for subsequent sample analysis, allowing quantitative and spatially resolved determination of the sulfur uptake derived from H2S or SO2 weathering with high sensitivity. The testing procedure is demonstrated on four different polymer types – polyimide (PI), polyvinylpyrrolidone (PVP), polystyrene (PS), and polyvinylacetate (PVA). Results from bulk measurements showed a significantly higher uptake of SO2 compared to H2S, and that the uptake increases with higher exposure temperatures and relative humidity levels. Further, quantitative sulfur depth profile measurements reveal diffusion profiles into the polymer films. The gas uptake was observed mostly withing the uppermost 1–2 µm of the surface, with quickly decreasing concentrations into the bulk of the materials. However, strong variations of the spatial distribution of sulfur between the different polymer types and between different exposure conditions were determined. The results provided give a good insight into what possibilities the proposed investigation procedure opens up for specifically dedicated research compared to standard methods that are used to determine gas permeation.

Computational modeling of microalgal biofilm growth in heterogeneous rotating algal biofilm reactors (RABRs) for wastewater treatment

Rotating algal biofilm reactors (RABRs) are innovative systems designed to cultivate microalgae biofilms efficiently. In this paper, we have developed a novel mathematical model to accurately capture the growth dynamics of algae biofilms within RABR. By considering the spatial heterogeneity of the RABR, we introduce a PDE-based model that addresses the spatial variations across the substratum, enabling a more accurate simulation of biofilm growth in RABRs. The photosynthesis process is modeled through reactive kinetics, driving the growth of the algae biofilm. To analyze the system's behavior, we employ finite difference numerical methods to solve the complex PDE model. We then conduct extensive numerical simulations to understand algae biofilm growth in the RABR environment under various operational factors and environmental conditions. One primary focus in these simulations is to investigate the impact of various harvesting strategies, harvesting frequencies, light intensity, and light exposure on the overall biomass productivity of the algae biofilm. The numerical results provide valuable insights into optimizing algae biofilm growth and designing harvesting techniques in RABR systems. Our proposed novel mathematical model provides an effective platform for the theoretical investigation and design of RABRs for wastewater treatment.