Search Results (8)

Water pollution is a significant environmental issue that has emerged because of industrial and economic growth. Human activities such as industrial, agricultural, and technological practices have increased the levels of pollutants in the environment, causing harm to both the environment and public health. Dyes and heavy metals are major contributors to water pollution. Organic dyes are a major concern because of their stability in water and their potential to absorb sunlight, increasing the temperature and disrupting the ecological balance. The presence of heavy metals in the production of textile dyes adds to the toxicity of the wastewater. Heavy metals are a global issue that can harm both human health and the environment and are mainly caused by urbanization and industrialization. To address this issue, researchers have focused on developing effective water treatment procedures, including adsorption, precipitation, and filtration. Among these methods, adsorption is a simple, efficient, and cheap method for removing organic dyes from water. Aerogels have shown potential as a promising adsorbent material because of their low density, high porosity, high surface area, low thermal and electrical conductivity, and ability to respond to external stimuli. Biomaterials such as cellulose, starch, chitosan, chitin, carrageenan, and graphene have been extensively studied for the production of sustainable aerogels for water treatment. Cellulose, which is abundant in nature, has received significant attention in recent years. This review highlights the potential of cellulose-based aerogels as a sustainable and efficient material for removing dyes and heavy metals from water during the treatment process. Full article

Dam reliability analysis is performed to determine the structural integrity of dams and, hence, to prevent dam failure. The Chenderoh Dam structure is divided into five parts: the left bank, right bank, spillway, intake section, and bottom outlet, with each element performing standalone functions to maintain the overall Dam’s continuous operation. This study presents a numerical reliability analysis of water dam reservoir banks using fluid–structure interaction (FSI) simulation of the bottom outlet structures operated at different discharge conditions. Three-dimensional computer-aided drawings were used to view the overall Chenderoh Dam. Next, a two-way fluid–structure interaction (FSI) model was developed to explore the influence of fluid flow and structural deformation on dam systems. The FSI modeling consists of Ansys Fluent and Ansys Structural modules to consider the boundary conditions separately. The reliability and performance of the reservoir bottom outlet structure was effectively simulated and recognised using FSI. The maximum stress on the bottom outlet section is 18.4 MPa, which is lower than the yield stress of mild steel of 370 MPa. Therefore, there will be no structural failure being observed on the bottom outlet section when the butterfly valve is fully closed. With a few exceptions, the FSI models projected that bottom outlet structures would be able to run under specified conditions without structural collapse or requiring interventions due to having lower stress than the material’s yield strength. Full article

Recently, the contribution of earthworms to plastic degradation and their capability to swallow smaller plastic fragments, known as microplastics, has been emphasized. The worm physically changes the size of microplastics and enhances microbial activities to increase the possibility of degradation. However, no research has shown that earthworms can chemically degrade microplastics to an element form, CO₂ or H₂O. In this review, previous research has been thoroughly explored to analyse the role that earthworms could play in plastic degradation in the soil. Earthworms can significantly affect the physical characteristics of plastics. However, earthworms’ abilities to chemically degrade or change the chemical structure of plastics and microplastics have not been observed. Additionally, earthworms exhibit selective feeding behaviour, avoiding areas containing a high plastics concentration and rejecting plastics. Consequently, earthworms’ abilities to adapt to the microplastics in soil in the environment can cause a problem. Based on this review, the challenges faced in earthworm application for plastic degradation are mostly expected to be associated with the toxicity and complexity of the plastic material and environmental factors, such as the moisture content of the soil and its temperature, microbial population, and feeding method. Full article

The renewed interest for power generation using renewables due to global trends provides an opportunity to rethink the approach to address the old yet existing load shedding problem. In the literature, limited studies are available that address the load shedding problem using a hybrid renewable energy system. This paper aims to fill this gap by proposing a techno-economic optimisation of a hybrid renewable energy system to mitigate the effect of load shedding at the distribution level. The proposed system in this work is configured using a photovoltaic array, wind turbines, an energy storage unit (of batteries), and a diesel generator system. The proposed system is equipped with a rule-based energy management scheme to ensure efficient utilisation and scheduling of the sources. The sizes of the photovoltaic array, wind turbine unit, and the batteries are optimised via the grasshopper optimisation algorithm based on the multi-criterion decision that includes loss of power supply probability, levelised cost of electricity, and payback period. The results for the actual case study in Quetta, Pakistan, show that the optimum sizes of the photovoltaic array, wind turbines, and the batteries are 35.75 kW, 10 kW, and 28.8 kWh, respectively. The sizes are based on the minimum values of levelised cost of electricity (6.64 cents/kWh), loss of power supply probability (0.0092), and payback period (7.4 years). These results are compared with conventional methods (generators, uninterruptible power supply, and a combined system of generator and uninterruptible power supply system) commonly used to deal with the load shedding problem. The results show that the renewable based hybrid system is a reliable and cost-effective option to address grid intermittency problem. Full article

Current development in precision agriculture has underscored the role of machine learning in crop yield prediction. Machine learning algorithms are capable of learning linear and nonlinear patterns in complex agro-meteorological data. However, the application of machine learning methods for predictive analysis is lacking in the oil palm industry. This work evaluated a supervised machine learning approach to develop an explainable and reusable oil palm yield prediction workflow. The input data included 12 weather and three soil moisture parameters along with 420 months of actual yield records of the study site. Multisource data and conventional machine learning techniques were coupled with an automated model selection process. The performance of two top regression models, namely Extra Tree and AdaBoost was evaluated using six statistical evaluation metrics. The prediction was followed by data preprocessing and feature selection. Selected regression models were compared with Random Forest, Gradient Boosting, Decision Tree, and other non-tree algorithms to prove the R² driven performance superiority of tree-based ensemble models. In addition, the learning process of the models was examined using model-based feature importance, learning curve, validation curve, residual analysis, and prediction error. Results indicated that rainfall frequency, root-zone soil moisture, and temperature could make a significant impact on oil palm yield. Most influential features that contributed to the prediction process are rainfall, cloud amount, number of rain days, wind speed, and root zone soil wetness. It is concluded that the means of machine learning have great potential for the application to predict oil palm yield using weather and soil moisture data. Full article

Old leachate normally has a low organic compound content, poor biodegradability and is hard to biologically treat. The efficacy of tetravalent metal salts as a coagulant and the application of a natural coagulant as a flocculant in landfill leachate treatment is still inconclusive. Hence, this study aimed to evaluate the potential application of tin tetrachloride (SnCl₄) as the main coagulant and the rubber seed (Hevea brasiliensis) (RS) as the natural coagulant aid as the sole treatment in eradicating highly coloured and turbid stabilised landfill leachate present at one of the old local landfills in Malaysia. The standard jar test conducted revealed that SnCl₄ was able to eliminate 99% and 97.3% of suspended solids (SS) and colour, respectively, at pH8, with 10,000 mg/L dosages, an average particle size of 2419 d·nm, and a zeta potential (ZP) of −0.4 mV. However, RS was found to be ineffective as the main coagulant and could only remove 46.7% of SS and 76.5% of colour at pH3 with 6000 mg/L dosages, and also exhibited smaller particles (933 d·nm) with ZP values of −6.3 mV. When used as a coagulant aid, the polymer bridging mechanism in RS helped in reducing the SnCl₄ concentration from 10,000 mg/L to 8000 mg/L by maintaining the same performances. The presence of 1000 mg/L RS as a coagulant aid was able to remove 100% of SS and 97.6% of colour. The study concluded that RS has the potential to be used together with SnCl₄ in treating concentrated leachate with SS and colour. Full article

Tin tetrachloride (SnCl₄) as a coagulant and rubber seed (Hevea brasiliensis) (RS), and polyacrylamide (PAM) as the coagulant aid were investigated in this work to treat matured and stabilised landfill leachate rich in COD and colour. A standard jar test was conducted at different pH values and dosages of coagulant/coagulant aid. When SnCl₄ acted as the primary coagulant, the optimum conditions occurred at pH 8 and 10,000 mg/L dosages, with 97.3% and 81% reductions of colour and COD, respectively. Both RS and PAM were not effective when used alone. When RS was used as the coagulant aid, the dosage of SnCl₄ was reduced to 8000 mg/L. The colour reduction was maintained at 97.6%, but the COD removal dropped to 43.1%. In comparison, when PAM was supplemented into 6000 mg/L SnCl₄, the reduction in colour was maintained at 97.6%, and the COD removal was almost at par when SnCl₄ was used alone. The addition of polymers as the coagulant aid helped in improving the sludge properties with a better settling rate (SSR) and larger flocs size. The decline of the SVI value indicates that less amount of sludge will be disposed of after the treatment. In addition, the rise of settling velocity (SSR) will reduce the size of the settling tank used in coagulation-flocculation treatment. Based on the results, it can be concluded that incorporation of coagulant aid into the treatment reduced the primary coagulant dosage without affecting the removal performances of pollutants. Full article

Machine learning (ML) offers new technologies in the precision agriculture domain with its intelligent algorithms and strong computation. Oil palm is one of the rich crops that is also emerging with modern technologies to meet global sustainability standards. This article presents a comprehensive review of research dedicated to the application of ML in the oil palm agricultural industry over the last decade (2011–2020). A systematic review was structured to answer seven predefined research questions by analysing 61 papers after applying exclusion criteria. The works analysed were categorized into two main groups: (1) regression analysis used to predict fruit yield, harvest time, oil yield, and seasonal impacts and (2) classification techniques to classify trees, fruit, disease levels, canopy, and land. Based on defined research questions, investigation of the reviewed literature included yearly distribution and geographical distribution of articles, highly adopted algorithms, input data, used features, and model performance evaluation criteria. Detailed quantitative–qualitative investigations have revealed that ML is still underutilised for predictive analysis of oil palm. However, smart systems integrated with machine vision and artificial intelligence are evolving to reform oil palm agri-business. This article offers an opportunity to understand the significance of ML in the oil palm agricultural industry and provides a roadmap for future research in this domain. Full article