Jegalakshimi Jewaratnam

Background: The booming palm oil industry is in line with the growing population worldwide and surge in demand. This leads to a massive generation of palm oil mill effluent (POME). POME is composed of sterilizer condensate (SC), separator sludge (SS), and hydro-cyclone wastewater (HCW). Comparatively, SS exhibits the highest organic content, resulting in various environmental impacts. However, past studies mainly focused on treating the final effluent. Therefore, this pioneering research investigated the optimization of pollutant removal in SS via different aspects of bioremediation, including experimental conditions, treatment efficiencies, mechanisms, and degradation pathways. Methods: A two-level factorial design was employed to optimize the removal of chemical oxygen demand (COD) and turbidity using Aspergillus niger. Bioremediation of SS was performed through submerged fermentation (SmF) under several independent variables, including temperature (20–40 °C), agitation speed (100–200 RPM), fermentation duration (72–240 h), and initial sample concentration (20–100%). The characteristics of the treated SS were then compared to that of raw sludge. Results: Optimal COD and turbidity removal were achieved at 37 °C 100 RPM, 156 h, and 100% sludge. The analysis of variance (ANOVA) revealed a significant effect of selective individual and interacting variables (p < 0.05). The highest COD and turbidity removal were 97.43% and 95.11%, respectively, with less than 5% error from the predicted values. Remarkably, the selected optimized conditions also reduced other polluting attributes, namely, biological oxygen demand (BOD), oil and grease (OG), color, and carbon content. In short, this study demonstrated the effectiveness of A. niger in treating SS through the application of a two-level factorial design.

This research aims to provide insights into the biological efficacy of a newly formed hydrophobic deep eutectic solvent (DES). A DES based on menthol was successfully synthesized with fatty acids. The DESs’ properties as enzyme activators were examined against a neat counterpart. The menthol:decanoic acid (1:1) combination showed improved thermal stability, strong catalytic activity, and reusability for up to four subsequent cycles under ideal conditions (pH 7.0, 40 °C for 2 h). The hydrophobic DES replaced hexane in ester synthesis, where RNL@DES5 showed better fatty acid conversion compared to neat RNL. This study demonstrated promising applications of hydrophobic DESs in non-aqueous organic reactions.

The use of various machines, equipment and power tools at TVET Institute causes the institute’s environment to be exposed to noise hazards that are similar to the industry. However, not much data has been published regarding noise exposure at TVET institutes. This study was carried out to document the noise exposure of work activities training in public TVET institutes in Malaysia that implement skill training programs in metal fabrication, furniture manufacturing and automotive maintenance. The identification of excessive noise, task-based noise exposure monitoring and source measurement was conducted. The noise contribution from each work activity to the daily A-weighted noise exposure level and sound pressure level emitted by machines and equipment was documented. The findings of this study recorded 20 activities with task-based noise contribution to the daily A-weighted noise exposure level between 75.3 dB and 95 dB. Based on the findings, the training environment at the TVET institutes has a risk of operating with excessive noise. The documented data can be used in planning the implementation of suitable noise control measures in TVET institutes.

Occupational noise-induced hearing loss (ONIHL) is the most reported occupational disease in Malaysia. ONIHL is aggravated by the presence of early hearing loss amongst the youth prior to entering a real working environment. At technical and vocational education training (TVET) institutions, students may develop early ONIHL because training workshops are designed imitating the industrial working environment to produce skilled workers. The exceeding noise level at workshops and recent risk of non-occupational noise can cause early ONIHL among these students. Therefore, ONIHL must be addressed at the early stage of producing skilled workers. Octa hearing conservation index (OHCI) system is developed as a management and monitoring tool for hearing conservation program (HCP) in TVET institutions. Six existing and two new HCP components were used to build the index system. A pilot test on the effectiveness of the OHCI system was conducted in a selected TVET institution for six months. The post-HCP shows a 52.6% improvement compared to the pre-HCP. The implementation of HCP has shown improved awareness on the hazards of loud noise exposure and active use of hearing protection devices among participants. The OHCI system has a great potential as a tool to improve HCP implementation in TVET institutions, and eventually, industry.

As the country started to become an export oriented country since the early 1970s, the growth of Malaysia’s textile industry has increased greatly. In 2011 alone, the country has provided a total of RM10.8 billion of exports of textile products and RM 6.6 billion of imports. This valuable improvement has lead to several environmental impacts involving land and water pollution. The current trend in waste management is to examine the feasibility of using textile sludge generated in wastewater treatment plants of textile industry as a partial replacement for clay as building materials. The chemical and physical properties of clay and textile have been analyzed. The effects of sludge proportion (0%-10%), and firing temperature (950oC and 1180oC) on the quality of the clay bricks were examined. The tests were conducted as per British Standard (BS) codes to investigate the potential of the textile sludge to be incorporate into clay for use as engineering blocks and load bearing bricks. According to the results, sludge addition and firing temperature are the important factors to determine the quality of clay bricks. All clay samples satisfied the requirement of British Standard norms in term of compressive strength and water absorption. It is seen that all bulk density of clay samples did not comply with good quality of clay brick but they can be categorized as lightweight building materials. Textile sludge was also tested for substitution into cement bricks. Cement samples with 10% textile sludge substitution comply with the requirement of British Standard norm for load bearing class 3. In summary, this study provides a preliminary research output that will contribute to expand a new area of research in recycling of textile sludge.

Microwave pyrolysis of oil palm fibre (OPF) was conducted to study the effect of microwave power, temperature, and nitrogen (N₂) flow rate on the production of hydrogen and biochar. The effect of microwave power ranging from 400 to 900 W, reaction temperatures ranging from 450°C to 700°C, and N₂ flow rates ranging 200 to 1,200 cm³ min⁻¹ were investigated. The microwave power, reaction temperature, and N₂ flow rates are directly proportional to hydrogen and inversely proportional to biochar yield. The characteristics of the biochar were analysed using CHNOS, scanning electron microscopy, and Brunauer–Emmett–Teller results. Central composite design was used to optimize the number of experiment. The optimized experimental datasets were used as inputs for developing a model that can predict the weight of hydrogen and biochar from microwave pyrolysis of OPF. A multilayer feedforward neural network model was developed by comparing the performance of 11 different types of backpropagation training algorithms. The 11 training algorithms belong to six classes, which are (a) additive momentum, (b) self‐adaptive learning rate, (c) resilient backpropagation, (d) conjugate gradient, (e) quasi‐Newton, and (f) Bayesian regulation (BR). Best performing training algorithm was selected based on the lowest error values computed. The objective of this research is to identify the most suitable training algorithm for this process. Levenberg–Marquardt and BR exhibited very good performance. BR exhibited best performance with lowest error values in predicting weight of hydrogen and biochar. The testing data error values for hydrogen weight is 0.216 root mean square error (RMSE), 0.144 mean absolute error (MAE), and 0.020 mean absolute percentage error (MAPE), and biochar is 0.886 RMSE, 0.489 MAE, and 0.019 MAPE. The optimum number of hidden neuron for this algorithm is 19 with mean square error 0.23. The biochar exhibits porous structure suggesting possible land application.