Engineering Science & Technology

An Accurate Measurement Technique for the Biological Oxygen Uptake Rate

Johnny Lee — 2025-11-21

For any wastewater treatment aeration tank, the paper proposes an accurate technique to deal with the Oxygen Uptake Rate (OUR) measurements. Since it measures the rate at which oxygen is used (in mg O²/L/hour), it is a useful tool to evaluate process performance, aeration equipment, and the biodegradability of the waste. Unfortunately, the literature abounds with examples of inconsistent measurement results. The manuscript observes that if a sample of mixed liquor is withdrawn from an aeration tank operating at low Dissolved Oxygen (DO) (dissolved oxygen), the OUR measured in the sample after shaking (or other means of perturbation) will be higher than the true OUR which is limited by oxygen supply. The composition of a sample of activated sludge being analyzed is continually changing, making it necessary to obtain measurements as quickly as possible at the site of the aeration basin. To alleviate the many problems in measurement, the proposed method using water dilution with saturated DO may give a more accurate measurement than the current standard method as described by the American Public Health Association (APHA). The discrepancy in the new method between the measured and the calculated SOTR_pw is in the assumed mole fraction of the exit gas of 0.19 which is reasonable but still based on guesswork. However, the discrepancies in the conventional method are that, the measured value of R is incorrect because of the inherent shortfall in the APHA (Biological Oxygen Demand (BOD) bottle shaking) technique, and it is more realistically given by the modified Eq. (2-3), which is originally stated as for a batch process provided by the American Society of Civil Engineers (ASCE) Guidelines, ASCE/Environmental and Water Resources Institute (EWRI) 18-18 recently published; and secondly, the incorrect driving force at the steady state, making the OTR at test conditions erroneously high. With a more accurate measurement of the OUR, it may be justified to modify the fundamental equation for oxygen transfer in a respiring system, as applied to the example given by the Guidelines. The specific content of the revised formula is proposed to be for a batch process.

Optimal Allocation of Clean Energy in Terms of Probabilistic Multi-Objective Optimization Method

Maosheng Zheng — 2025-11-06

In this paper, the Probabilistic Multi-Objective Optimization method (PMOO) is applied to perform the optimal allocation of clean energy with multiple objectives. A solar photo-thermal system, wind energy, and a comprehensive energy storage system for photo-thermal power generation are involved. In PMOO, a new concept of "preferable probability" is put forward to address the preference degree of an attribute of a candidate and the corresponding evaluation method and attributes of the alternative scheme are divided into two types, i.e., beneficial type and unbeneficial (cost) type of attributes, and the corresponding evaluation algorithms of their partial preferable probability are formulated quantitatively. The total preferable probability of each alternative scheme is the product of all possible partial preferable probability, which is employed as the unique indicator to conduct the ranking of the optimization. In the application of optimum allocation problem of clean energy, the solar energy assurance rate and efficiency index of the heating system are the optimal criteria to be maximized, while the heat collecting area of solar collector, the heating capacity of heat pump and the volume of water tank for heat storage are used as input parameters. Especially, the range analysis of the total preferable probability of each alternative scheme is conducted using orthogonal experimental design. The result indicates the optimum configuration for this allocation of clean energy design. Alternatively, in the application of wind-photo-thermal power generation and storage comprehensive energy system problem, both carbon emissions and total operating costs are the optimization criteria to be minimized for the three scenarios, yielding an optimal configuration.

Design of a Tiny House Generator with Location Parameterisation Function

Maximilian Frank Leon Schäfer — 2025-11-03

In light of growing housing shortages and rising rental prices, alternative housing forms such as Tiny Houses are becoming increasingly popular. This housing form is characterised by compact floor plans, with sizes typically under 45 m² (480 sqft) per person. As the trend originated in the USA, much of the literature and design proposals refer to the prevalent climate conditions found there. This research aims to bridge this gap by developing a script that generates a proposal for a Tiny House for any given location, using construction strategies adapted to the local climate. First, an analysis was conducted to determine which building components of a Tiny House are particularly susceptible to climatic influences and how specific weather conditions affect these components. Based on four case studies and relevant literature, parametric construction principles were developed. These principles were incorporated into a script that used weather and climate data to generate a 3D model of the Tiny House. The script was implemented within the Grasshopper environment of the 3D modelling software Rhino 3D. To provide a user-friendly interface, it was integrated into a web application. This allows users to select locations and various input parameters, to visualize the generated model, as well as to access detailed information about the construction decisions and how they are influenced by the local climate. To exemplify the output generated by the tool, three models for different locations were selected and slightly modified to show how these buildings might be built and look in reality. The thesis was successful in developing a fully parametric building generator, which can further be expanded to include features such as complete indoor climate simulations. The script and implementation are fully documented. However, given the general complexity of architecture and construction, the question arises as to whether a future approach based on artificial intelligence might be more effective than the algorithmic approach taken here.

Harnessing Soil Organic Carbon to Build a More Productive and Climate-Resilient Future

Sanjay Saxsena — 2025-12-31

Soil Organic Carbon (SOC) is a critical determinant of soil fertility, ecosystem stability, and climate regulation. This study analyzes the spatial and temporal dynamics of SOC in the Tonk District of Rajasthan, India, using the Trend. Earth platform integrated with satellite-derived Land-Use and Land-Cover (LULC) data for the period 2001- 2020. Statistical trend analyses (Mann-Kendall, Sen’s Slope, and Pearson correlation) indicate that 98.31% ± 0.17% of the area has remained stable in SOC content, demonstrating high soil resilience, while 1.63% ± 0.17% shows measurable degradation, largely influenced by wind erosion and intensive agricultural activity. Land-use transition analysis further reveals 54.79 km2 of urban expansion and 114.52 km2 of reduced irrigated cropland, reflecting growing anthropogenic pressure on soil resources. These findings emphasize the need for targeted soil conservation, agro-forestry adoption, and integrated land-management policies to sustain soil carbon stocks. The study demonstrates the applicability of Trend. Earth-based geospatial monitoring for evidence-driven SOC assessment and regional climate resilience planning in semi-arid environments

Towards Sustainable End-of-Life Management of Wind Turbine Blades Through Circular Economy Strategies: The Case Study of Indonesian Wind Farms

Ayu Andira — 2025-12-30

Indonesia’s transition to renewable energy, highlighted by the operation of Sidrap and Tolo 1 wind farms, faces a significant challenge: managing the end-of-life of wind turbine blades. These blades, primarily composed of Glass Fibre-Reinforced Plastic (GFRP), are challenging to recycle due to their composite structure. A circular economy framework must be developed to address this issue, focusing on material recirculation and waste reduction. This study explores upcycling strategies as a core component of the framework, aiming to repurpose decommissioned blades into functional products while minimising environmental impact. Using secondary data and Life Cycle Assessment (LCA), three upcycling scenarios are assessed: turning blades into pedestrian bridges, housing foundations, and fishing vessels. Each scenario shows considerable potential for reducing CO2 emissions by replacing traditional materials like steel, concrete, and wood with repurposed blade components. The results highlight the practicality and environmental advantages of applying circular economy principles to Indonesia’s wind energy sector. Developing a strong framework for blade upcycling not only encourages sustainable infrastructure but also strengthens Indonesia’s dedication to renewable energy systems. This approach provides a scalable model for other regions encountering similar challenges in renewable energy waste management.The system boundaries are limited to the material substitution phase and exclude upstream and downstream processes such as blade cutting, transportation, installation, and maintenance. This simplification is intended to isolate the environmental benefits of material replacement and align with similar comparative studies. However, it is acknowledged that these excluded processes, especially for large, heavy blade sections, can contribute significantly to the overall environmental impact. Their omission represents a limitation of this study and may lead to an underestimation of total emissions. Future research should incorporate these phases for a more comprehensive assessment.

RNN-Enhanced Takagi-Sugeno Fuzzy Control for Tesla Model 3 Car-Like WMR Dynamics

Mohamed Waled Aly Ramadan — 2025-12-30

This paper investigates the integration of machine learning to enhance the performance of fuzzy control systems, specifically for steering control for Car-Like Wheeled Mobile Robot (WMR) dynamics. A Takagi-Sugeno fuzzy controller is employed to effectively manage the dynamic behavior of a Tesla Model 3, successfully controlling its steering across multiple trials. The non-linear vehicle model consistently follows the desired trajectories dictated by the fuzzy controller. To further enhance performance, data consisting of control signals and errors generated by the fuzzy controller are collected for various steering angles, and used to train a Recurrent Neural Network (RNN) to emulate and replace the controller. The trained RNN is subsequently tested on previously unseen steering angles, demonstrating a rapid response and accurate control signal generation. Remarkably, the RNN successfully generalizes beyond its training range, providing reliable control for steering angles outside the range of the training data. This study highlights the potential of hybrid fuzzy-Artificial Neural Network (ANN) systems to enhance the adaptability and efficiency of control strategies in autonomous vehicle applications.

From Neat Epoxy to Nanocomposites: Innovations in Bonding Systems for FRP-based Concrete Retrofitting: A Mini Review

Mohammad Al-Zu'bi — 2025-11-21

Structural retrofitting with Fiber-Reinforced Polymer (FRP) systems has become a vital approach for enhancing the strength, ductility, and durability of deteriorating concrete structures. The efficiency of these systems largely depends on the adhesive layer, where conventional Neat Epoxy (NE) adhesives often suffer from brittleness and limited crack resistance. Recent advancements in Nanomaterial-Modified Epoxy Adhesives (NMEAs) have led to notable improvements in their mechanical, thermal, and interfacial properties. Incorporating carbon-based nanomaterials such as Carbon Nanotubes (CNTs), Carbon Nanofibers (CNFs), and graphene has been shown to enhance fracture toughness, tensile strength, and load transfer. For example, introducing 0.1 wt.% single-walled CNTs resulted in a 13% increase in fracture toughness and a 3.5% improvement in compression-after-impact strength, while 0.5 wt.% multi-walled CNTs achieved up to 7% higher elastic modulus and 10% greater tensile strength compared to NE. Similarly, silicon-based nanomaterials, including silica nanoparticles and nanoclays, enhance stiffness, minimize porosity, and improve adhesion efficiency in both Externally Bonded Reinforcement (EBR) and Near-Surface Mounted (NSM) FRP systems. Spectroscopic and microstructural analyses reveal that nanoparticles influence cross-linking and crystallinity within the epoxy matrix, leading to more stable and durable adhesive bonds. Beyond mechanical enhancement, eco-friendly nanomaterials, such as rice husk ash-derived silica and biomass-based graphene, contribute to sustainability by lowering embodied carbon and extending the structural lifespan. This mini-review synthesizes recent developments, identifies critical research gaps, and outlines future directions toward resilient, high-performance, and sustainable FRP-retrofitting systems.

Advanced Oxidation Processes and Treatment Strategies for Hospital Wastewater: A Comprehensive Review

Mahmoud Bali — 2025-12-31

Because Hospital Wastewater (HWW) is a complex mixture of chemicals, pharmaceutical residues, radioisotopes, and pathogens, it poses a serious environmental risk. Especially during epidemics, its unregulated discharge can contaminate water supplies and promote the spread of antibiotic-resistant microorganisms. Pharmaceuticals, endocrine disruptors, and persistent organic pollutants, which are present even at low concentrations but have high hazardous potential, are examples of these emerging contaminants, widespread in both developed and developing countries. Aquatic ecosystems are disrupted by the multitude of macro-pollutants (heavy metals, hormones, detergents) and micro-pollutants such as Chemical Oxygen Demand (COD), Total Suspended Solids (TSS), and nitrogen found in HWW. Advanced Oxidation Processes (AOPs) have become an increasingly popular technique for degrading harmful pollutants. Even though lab results are encouraging, further study is required before widespread application. This review discusses published research on AOPs for emerging pollutants in HWW, highlighting gaps in detection, optimization, and practical implementation, and emphasizing how future studies in these areas could help protect water resources and improve HWW management.

Integrating AI in Energy Efficiency, Natural Hazards and Ecological Resilience: A Python Case Study

Evangelos Tsiaras — 2025-12-15

The integration of Artificial Intelligence (AI) into sustainability science offers significant potential for improving prediction accuracy, resource allocation, and decision-making across environmental domains. This study develops a unified, Python-based AI methodology and applies it to three representative challenges: energy consumption forecasting, wildfire occurrence prediction, and ecological resilience assessment. For each case, publicly available datasets were preprocessed through outlier removal, missing-value imputation, and feature normalization. Appropriate machine learning models-linear regression, logistic regression, and multiple linear regression-were implemented to predict key outcomes, with performance evaluated using accuracy, coefficient of determination (R² ), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). The study deliberately employs baseline, interpretable AI models-linear regression, logistic regression, and multiple linear regression-to ensure methodological transparency and highlight the generalizable workflow rather than algorithmic complexity. The results demonstrate that a standardized AI workflow can be adapted to diverse environmental contexts, while at the same time it maintains interpretability and strong predictive performance. The novelty of this work lies in its methodological generalization, enabling cross-domain application, promoting reproducibility, and providing a scalable decision-support tool for researchers and policymakers. The proposed framework offers a practical pathway for integrating AI into sustainability planning, bridging the gap between domain-specific studies and transferable, replicable solutions.