Engineering Research: Perspectives on Recent Advances Vol. 12

Methylene Blue Adsorption Utilising Enhanced Chitosan Beads: A Response Surface Methodology and Artificial Neural Network Study

Ephraim Igberase — 2026-01-14

Pollution emanating from cationic dyes, including Methylene Blue (MB), in the environment causes many health problems. The release of MB into natural water bodies is destructive to natural creatures and ecosystems. Adsorption is one such technique, which has advantages such as low cost, environmentally friendly material for dye removal, and ease of use. Chitosan is a cost-effective, environmentally friendly adsorbent with high surface area, excellent adsorption capacity, and good mechanical stability. A method was established to assess the adsorption of methylene blue (MB) from synthetic wastewater. Chitosan beads (CS) were synthesised, cross-linked with glutaraldehyde (CCS), and subsequently grafted with aniline (GCCS). The characteristics of the synthesised materials were evaluated using XRD and BET techniques. The research examined pH, contact time, adsorbent amount, and starting concentration. The input data consisted of these parameters, whereas the output data was determined by MB removal efficiency. Response surface methodology/central composite design (RSM-CCD) and artificial neural network (ANN) were utilised to predict and optimise MB adsorption. Statistical indicators evaluated the significance of these models. In developing the ANN model, 70% of the data was designated for training, 15% for validation, and 15% for testing. The RSM-CCD data indicate that the optimal process parameters were achieved at a pH of 7, an adsorbent dose of 6 g, a contact period of 55 minutes, and a 125 mg/L starting concentration. Thus, training, testing, and validation phases characterise a well-trained neural network, with R² values recorded at 1, 0.96837, and 0.96146, respectively. The statistical results indicated that the ANN method surpasses the RSM model technique.

Intelligent Battery Management in Electric Vehicles: A Comprehensive Review of AI Techniques

Ashok Kumar Bandla — 2026-01-14

To reduce carbon emissions and tackle global environmental problems, the automotive sector has focused heavily on electric vehicles (EVs). However, the eventual deterioration of battery health and performance may adversely affect the efficiency of EVs. Due to their ability to accurately assess battery health, analyse faults, and control temperature for enhanced safety, reliability, and effective optimisation of EV performance, artificial intelligence (AI) techniques have garnered significant interest. This review investigates and evaluates the effects of AI techniques to improve the battery management system (BMS) of electric vehicles (EVs). A variety of methodologies are employed to perform a statistical analysis of relevant BMS papers. The statistical analysis assesses essential characteristics such as current research trends, keyword analysis, nation analysis, authorship, collaboration, publishers, and research classification. Moreover, a thorough examination of the goals, contributions, advantages, and disadvantages of advanced AI methods is provided. In addition, several key guidelines and recommendations are presented, along with a number of significant concerns and challenges, for potential future enhancement. Future researchers could utilise the statistical analysis as a guide to develop innovative BMS technologies for EVs that function and are managed sustainably.

Enhanced Inspection of Buried Pipelines Subjected to Electromagnetic Interference from Transmission Power Lines and External Cathodic Protection System

Anderson Teixeira Kreischer — 2026-01-14

Using the combined CIPS-DCVG (Close Interval Potential Survey)-(Direct Current Gradient Potential) technique provides data on the cathodic protection system's potential and information about possible defects in the pipeline coating. The great advantage of the combined inspection is that both techniques are performed simultaneously under the same climatic and soil conditions, eliminating spatial error. However, in order to properly assess the integrity of the pipeline, the data provided by the combined technique must be accurate. In regions with electromagnetic interference, the quality of the data collected in the field may be compromised. This study demonstrates through a practical example that the effects of electromagnetic interference on data collected in the CIPS-DCVG survey can be mitigated by using GPS-synchronised stationary data loggers at gas pipeline cathodic protection system measurement points.

Price Adjustment Mechanism in Assessing Romanian Public Procurement of Construction Contracts

Cornel Adrian Ciurusniuc — 2026-01-14

Following the entry into force of Romania’s Public Procurement Law 98/2016, the use of price adjustment provisions in construction contracts has become a standard practice. This chapter, which presents a comprehensive analysis of the financial implications of eight adjustment formulas applied to public construction projects executed over three durations (12, 24, and 36 months) between 2018 and 2024, is a significant contribution to the field. The analysis was based on a review of legal documents and public procurement contracts between 2016 and 2024. Statistical data sourced from the Romanian National Institute of Statistics (INS), including monthly indices for construction materials, labour, and fuel. Calculations were performed for no advance payments and with standardised profit margins of 5%. The comparative analysis using objective indices published by Romania’s National Institute of Statistics reveals the impact of inflation and cost variations on adjusted contract values. Three scenarios, each starting in different years (2018, 2020, and 2022), are explored to determine the sensitivity of the formulas to market fluctuations. Results show that by applying the eight adjustment formulas, only two formulas tend toward annual inflation. The indices used by the construction branch are not correlated with yearly inflation, and when no advance payments are granted, they offer a reliable basis for economic equilibrium in public contracting. The impact of inflation puts great pressure on works contracts, especially on adjustment formulas, for which it is necessary to carry out impact studies.

Investigating the Optimum Energy Potential of an Operating Digester for a Slaughterhouse

Moses Jeremiah Barasa Kabeyi — 2026-01-14

Waste from slaughterhouses is a global concern as the sector generates huge quantities of animal organic waste, often on a daily basis. Slaughterhouse waste has high organic content, making it an attractive feedstock for anaerobic digestion (AD), which produces biogas through a series of biochemical processes. In this study, the performance of an operating biogas plant using slaughterhouse waste was investigated to improve its operation and hence protect the environment from pollution coming from slaughterhouse waste and provide energy and potential revenue to the slaughterhouse facility. Important parameters for a biodigester that were investigated in the performance analysis are the design specification, actual production, feedstock availability and environmental impact of the facility. The study was conducted at Nyongara slaughterhouse, Dagoretti, located some 26km away from Nairobi City centre. Both primary and secondary data were collected and used in the research. Data was analysed and presented using descriptive statistics. The collected data were compared to those of an ideal biogas plant. The study established that the existing and operating biogas plant is not operating at optimum conditions. The slaughterhouse generates about 56,000 kg of solid and liquid waste daily, out of which only 2,800 kg is utilised, which represents just 5% waste utilisation by the biodigester. The biodigester performance is about 70% of the optimum biogas production, while only 5% of biodegradable waste is digested, leaving 95% for disposal, undigested, leading to severe environmental pollution and high disposal costs. The investigation recommends increase in average digestion temperature to 37°C from 34°C, change of substrate to water mixing ratio from 1:2.5 to 1:1, increase of the pH from average of 6.5 to 7, reduction in hydraulic pressure from 400 mm to 350 mm of water by reducing average height of the substrate in the digester and increase in residence time from 17 to 20 days. The proposed modifications to the digestion operating conditions are expected to increase the biogas production from 35 m³ per day to 48 m³ per day, which is a 37% increase in production.

Understanding the Application of the Discrete Element Method for Modelling Lithium-ion Batteries in Electric Vehicles: A Critical Review

Fernández Gómez Tomas — 2026-01-14

Lithium-ion batteries are a type of rechargeable battery that uses lithium as a storage and energy generator. These batteries work by transferring lithium ions between a cathode and an anode through an electrolyte, generating energy during discharge and storing lithium during charging. Lithium-ion batteries have transformed the foundations of the world's infrastructure. From energy production to electricity distribution, the transportation of people and goods, and the operation of computing devices and even the internet. This scenario was analysed during the aforementioned conference, where the latest innovations in the battery field—sodium, cobalt-free, and even solid-state batteries—were the main focus of the event. The purpose of this project is the application of the discrete element method (DEM) for the realisation of a particle model based on the electrode material of the lithium-ion batteries in electric cars, trying to solve the main problems they present during charging and discharging, since these directly affect the lifetime of the same. The study concluded that the model developed has reliable foundations that can be implemented and, in turn, improved to obtain more accurate results.

Green Textile Finishing: Utilisation of Vetiver Extract for Antimicrobial Home Textile Applications

Krishnaveni Vasudevan — 2026-01-14

Home textiles represent one of the fastest-growing segments within the technical textile industry. The home-tech sector includes textile materials intended for household and hygiene-related uses, where sustainability and safety are key requirements. Growing consumer awareness regarding health and cleanliness has increased the demand for textile products with built-in antimicrobial functionality. Among natural plant resources, Vetiveria zizanioides is an abundant and valuable source known for its strong fibres, pleasant fragrance, and inherent antimicrobial properties. In this study, extracts were prepared from Vetiveria zizanioides powder using a suitable solvent system. The extract was subjected to phytochemical screening and chemical component analysis, followed by evaluation of its antimicrobial potential against selected standard microorganisms. Both qualitative and quantitative antifungal tests were conducted on treated fabrics against Aspergillus niger and Candida albicans, while antibacterial activity was assessed against Staphylococcus aureus (ATCC 25923) and Escherichia coli (ATCC 25922), representing Gram-positive and Gram-negative bacteria, respectively. The findings demonstrate the effectiveness of vetiver-based treatments and highlight their potential application in medical and home textile products.

Comparative Kinetic Evaluation and Bio-energy Potential of Anaerobic Co-digestion of Cow Dung with Invasive Water Hyacinth and Agro-industrial Cassava Peels

Stephen Oyelami — 2026-01-14

Third-world areas are facing a two-fold problem in terms of how the organic waste streams can be handled and how they can be provided with predictable and decentralised energy. This research paper is a comparative kinetic evaluation of the anaerobic co-digestion of cow dung (CD) with 2 different high-carbon feedstuffs, cassava peel (CP) and water hyacinth (WH), on the stability, performance, and fuel quality of biogas produced under mesophilic batch anaerobic digestion. Three digestion systems were operated for 30 days: cow dung alone (CD), cow dung with cassava peel (CD+CP), and cow dung with water hyacinth (CD+WH). Daily monitoring of temperature, pH, total dissolved solids (TDS), electrical conductivity (EC), and gas yield provided insights into microbial activity and substrate behaviour. All digesters maintained constant mesophilic conditions (25 - 32 °C) and buffered pH values (7.2 - 8.9) for most of the retention period, with a final decline (≈5.2 - 6.1) marking substrate depletion. Co‑digestion markedly improved biogas productivity compared to mono‑digestion. The CD+WH blend produced 4,810 L of biogas (72.8% increase), with steady gas release linked to the gradual breakdown of lignocellulosic material. The CD+CP blend achieved the highest yield at 5,042 L (81.1% increase), driven by the rapid fermentation of cassava starch. Gas composition analysis showed methane concentrations peaking at 61.0% in CD+CP, compared with 52.0% in CD alone. However, all raw gases contained critically high hydrogen sulfide (5,000 - 8,000 ppm). A low-cost, locally designed and fabricated iron-oxide scrubber used during this work successfully eliminated H₂S by reducing the concentration from about 8000 ppm to 0 ppm. Post-scrubbing flammability tests confirmed high-quality fuel, with co‑digested gases producing strong blue flames characteristic of methane-rich biogas. Beyond energy recovery, the study also demonstrated the agronomic value of the digester effluents. The nutrient-rich slurry was applied as fertiliser to plants, improving soil quality and supporting healthy growth, thereby closing the resource loop between waste management, energy generation, and agriculture. In addition to energy recovery, the nutrient-rich digester effluents were applied as fertiliser, improving soil quality and supporting plant growth. The findings demonstrate that co-digestion of cow dung with cassava peel or water hyacinth enhances biogas yield, methane content, and process stability while generating valuable organic fertiliser. This integrated approach supports decentralised renewable energy and sustainable agriculture in biomass-rich regions.

Gas Pollution: A Parametric Analysis Adopting Drone-based Evaluation and IOTs

Bassey Okon — 2026-01-14

In Nigeria, the Niger Delta region is faced with challenges of oil and gas exploitation. These activities increasingly affect human, aquatic life in the ocean, animals and the natural environment. Recent advancements in technology have introduced unmanned aerial vehicles (UAVs), commonly known as drones, as a viable and innovative solution to these challenges. This study presents a novel approach for monitoring air pollution with a drone and Internet of Things (IoT) technology. The specific objectives include developing a drone-based system capable of capturing gas pollution data, integrating various sensors to monitor environmental conditions and detect air pollutants (harmful gases) and developing a communication system for real-time data collection and dissemination. The design utilises sensors for the detection of hazardous gases and an ESP8266 module for real-time data transmission and cloud-based data presentation. The system facilitates sustainable environmental studies by providing access to areas that are hard or unsafe to reach, anytime. The research locations include Iko Town and Ukpenekang communities in Eastern Obolo LGA, Akwa Ibom State, Nigeria. Data collected during drone test flights was compared with traditional air quality monitoring stations to evaluate accuracy. The results show an affordable method for measurement of air quality in real time, especially in the challenging areas that are affected by oil and gas exploration, production and refining processes, such as the Niger Delta region of Nigeria. The findings demonstrate the feasibility of using drones and IOT for real-time environmental monitoring aimed at equipping researchers and policy makers with data to protect human lives, public health and the environment. In the course of this study, minor limitations were observed, including restricted flight duration and short range. Future research will investigate advancements in long-range communication protocols and the application of machine learning technology.