Search

Abstract In this study, sandwich structures with commercial-grade aluminium alloy skins and bio-inspired core (mycofoam) were fabricated and tested to obtain the axial compression response in terms of in-plane deformation measures and stress. The ensuing spectrum of response data from experimental tests were then fed into three different data driven models that include simple linear regression (SLR), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). The performance of the models is compared in estimating the compressive response of sandwich panels with the mycofoam. To assess the performance of models, coefficient of determination (R2), root mean squared error (RMSE) and mean absolute error (MAE) are used. Eleven different training algorithms are tested in ANN and Bayesian Regularization backpropagation with 9 hidden neurons is found to be the optimum ANN structure. In ANFIS model, triangular-shaped membership function (MF) with 20 rules gives the highest performance among 8 different MFs. All three models are found to be capable in estimating the compressive response. ANFIS model has the highest performance, followed by ANN model then SLR model with R2, RMSE and MAE being 0.9999, 0.0818, 0.0415 for the training dataset; 0.9999, 0.1626, 0.0491 for the testing dataset and 0.9999, 0.0943, 0.0437 for the validation dataset, respectively. [ABSTRACT FROM AUTHOR]

Palm oil extraction is one of the important industries in Malaysia. The extraction of palm oil will not only result in the production of palm oil for human consumption but also generate palm oil mill effluent (POME), which contains highly polluting properties. Synthetic coagulants such as aluminum sulfate (alum), aluminum chloride, and sodium aluminate contribute toward environmental pollution and risk to human health. Thus, natural coagulants and flocculants have been introduced throughout the globe to slowly replace synthetic coagulants. In this study, rambutan (Nephelium lappaceum) seed was used as natural coagulants combining with aloe vera as natural flocculants. The standard jar test experiments were conducted to study the effects of pH, coagulant dosages, and rapid mixing speed on the removal efficiency of chemical oxygen demand (COD), total suspension solid (TSS), and turbidity of POME. The model of the coagulation–flocculation process was evaluated by using response surface methodology (RSM) and artificial neural network (ANN). RSM associated with Box–Behnken design was used to identify the optimum parameters (inputs), which consist of pH, coagulants dosages and rapid mixing speed for the optimum removal efficiency (outputs) of COD, TSS, and turbidity of POME. In terms of removal efficiency of COD, TSS, and turbidity of POME, rambutan seed has the optimum removal efficiency of 28.48%, 56.38%, and 60.36%, respectively. ANN was utilized with the aim of evaluating the performance of 12 training algorithms to predict the output results. Levenberg–Marquardt (LM) training algorithm was verified to be the best training algorithm for coagulation–flocculation process of rambutan seed based on the lowest mean squared error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) for validation data and the correlation coefficient R of.9347. [ABSTRACT FROM AUTHOR]

Artificial neural network (ANN) was used to predict the biofilm communities present in the microbial fuel cells (MFCs), as well as the power generation from wastewater treatment. The ANN model was able to predict the total abundances of seven exoelectrogenic bacteria-associated genera, viz. Anaeromyxobacter, Bacillus, Clostridium, Comamonas, Desulfuromonas, Geobacter , and Pseudomonas for the MFCs based on the physicochemical properties of the sludge inocula, with accuracies in the range of 62∼92%. An additional ANN model was developed to integrate the biofilm results and predict the power generation from wastewater, with an accuracy of 84% when validating with literature studies. The results show that ANN is a useful tool for predicting the biofilm communities and power generation from MFCs, thus avoiding the necessity of conducting complex biofilm metagenome analysis, and greatly aiding future parametric investigation and scale-up studies. • Artificial neural network (ANN) model predicted power from microbial fuel cells. • ANN model predicted inoculum's biofilm communities from physicochemical properties. • Avoiding the necessity of complex metagenome analysis. • Correlating biofilm community in MFCs with power generation. • Models are potentially refined and tailored for real applications using wastewater. [ABSTRACT FROM AUTHOR]

This study presents a novel approach to optimize the anaerobic digestion of palm oil mill effluent (POME) for maximum biogas production on an industrial scale. Unlike most optimization studies which are limited to laboratory scale, this study utilized data from a real-world industrial setting to simulate the anaerobic digestion (AD) process. The processes include feedstock pre-treatment, biogas scrubbing, wastewater treatment, and sludge handling. The impact of process conditions such as hydraulic retention time (HRT), organic loading time (OLR), anaerobic sludge recycling ratio (RR AS), treatment effluent recycling ratio (RR TE), and reaction temperature on chemical oxygen demand (COD) removal and methane yield was analysed. An integration of response surface methodology (RSM) and artificial neural network (ANN) is applied to predict the optimal values for the process conditions. The results showed that the optimal values for HRT, temperature, OLR, RR AS , and RR TE are 38.58 days, 45 °C, 1.033 g COD/(L∙day), 0.052, and 0.95, respectively. The optimization improves the COD removal efficiency, SS removal efficiency, and biogas production by 7.38%, 8.37% and 16.18%, respectively when processing 24,134 L/h of POME feed. The optimized AD process is economically viable with a payback period of 5.34 years and a net present value (NPV) of $5,079,000. A framework that incorporates response surface methodology and machine learning was developed to optimize a palm oil mill effluent anaerobic digestion plant, which produces methane-rich biogas that can be converted to electricity through combustion. [Display omitted] • Simulation was employed for the design of experiments (DOE) in anaerobic digestion. • Optimal process conditions were predicted via integrated ANN and RSM approach. • ANN and RSM enhanced the biogas production by 14.95% and 16.18%, respectively. • The optimized plant achieves higher net present value than the base plant. [ABSTRACT FROM AUTHOR]

This work emphasizes on reactor scale synthesis of polycaprolactone using ε-caprolactone as monomer and Candida antarctica lipase B enzyme as catalyst. The bio-polymerization was performed in 2 L thermostatic reactor by the conventional method with constant stirring. Optimisation of process parameters such as reactor temperature, reaction time, and mixing speed was carried out using response surface methodology based on D-optimal design. Molecular weight of the bio-polymer was chosen as the response for optimization, which is later characterised by gel permeation chromatography. The results show good agreement between predicted values and actual values obtained through the experiments. [ABSTRACT FROM AUTHOR]

At present, polyhydroxyalkanoates (PHAs) have been considered as a promising alternative to conventional plastics due to their diverse variability in structure and rapid biodegradation. To ensure cost competitiveness in the market, thermoseparating aqueous two-phase extraction (ATPE) with the advantages of being mild and environmental-friendly was suggested as the primary isolation and purification tool for PHAs. Utilizing two-level full factorial design, this work studied the influence and interaction between four independent variables on the partitioning behavior of PHAs. Based on the experimental results, feed forward neural network (FFNN) was used to develop an empirical model of PHAs based on the ATPE thermoseparating input-output parameter. In this case, bootstrap resampling technique was used to generate more data. At the conditions of 15 wt % phosphate salt, 18 wt % ethylene oxide-propylene oxide (EOPO), and pH 10 without the addition of NaCl, the purification and recovery of PHAs achieved a highest yield of 93.9%. Overall, the statistical analysis demonstrated that the phosphate concentration and thermoseparating polymer concentration were the most significant parameters due to their individual influence and synergistic interaction between them on all the response variables. The final results of the FFNN model showed the ability of the model to seamlessly generalize the relationship between the input-output of the process. [ABSTRACT FROM AUTHOR]

In recent years, machine learning (ML) techniques have been developed to predict the performance of anaerobic digestion (AD) processes including methane potential and reactor stability. However, their practical applications to industrial-scale palm oil mill effluent (POME) treatment plant are limited. In this study, ML algorithms such as response surface methodology (RSM), adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) are employed to model the biogas production and methane yield from the AD of POME in a local industry-scale anaerobic covered lagoon. Results demonstrated that these models were well aligned with two years of operational data with high coefficient of determination (R2) of up to 0.98. ANFIS yields the highest prediction accuracy, with R2 of 0.9791 along with the lowest mean absolute error (MAE) of 0.0730 and root mean squared error (RMSE) of 0.1438. Subsequently, ANFIS is used in the multi-objective optimisation to maximise the biogas production and methane yield. Optimal conditions for the temperature of the anaerobic digester, pH and recirculation ratio are 38.9 °C, 7.03 and 1.89 respectively which could enhance the biogas production and methane yield by 19.4% and 12.2% respectively. Confirmatory experiments were carried out in the biogas plant under this set of optimised variables for a period of two months. The predicted biogas production and methane yield are highly correlated to the actual data with small percentage difference of 1.25% and 5.09% respectively, indicating that ANFIS model was accurate and reliable. Sensitivity analysis shows that pH has the most dominant effect on the methane yield. [Display omitted] • Various machine learning models have been applied for prediction and optimisation. • ANFIS yields the highest prediction accuracy. • Biogas production and methane yield are enhanced by 19.4% and 12.2% respectively. [ABSTRACT FROM AUTHOR]

Polyhydroxyalkanoates (PHA) are naturally occurring biopolymers, obtained from microorganisms. Properties like biodegradability and biocompatibility make PHA a part of today's commercial polymer industry. However, the production cost of PHA has been a great barrier to extend its application to large scale production. Substrates and usage of pure cultures constitute the main reason for its high production cost. On the other hand, rapid industrialization i.e., industrial sectors such as sugar, pulp and paper, fruit and food processing, dairies, slaughterhouses, and poultries, has resulted in the generation of the huge quantity of wastes. Consequently, becoming large source of environmental pollution and health hazard. This review emphasizes on the usage of various waste feedstocks obtained from industrial and agricultural industries as an alternate substrate for PHA production. As these waste materials are rich in organic material and also microbes, they can be the good starting material for PHA production. Additionally, advantages and economic importance of mixed cultures and also PHA applications are discussed. Future prospects and challenges in PHA production from waste feedstocks are also highlighted. [ABSTRACT FROM AUTHOR]

• Microwave heating method offers many benefits as compared to conventional heating. • Microwave-assisted pyrolysis (MAP) can be employed to turn waste into biofuels. • Carbon material like biochar can be used as both a catalyst and microwave absorber. • There is almost 91% recovery of energy from biomass in the MAP process. • Hydrocarbons present in bio-oil and syngas serve for carbon nanotube formation. The current scenario of environment urgently needs alternative biologically synthesized fuels, value-added products, and preparation of catalyst to create a pollution free environment. Usually, several methods are available for the synthesis of various commodities from many biological resources. Microwave-assisted pyrolysis (MAP) is a relatively new process and has emerged as a promising technique to transform biomass feedstock into biofuels, including bio-oil, syngas and biochar. This paper provides a state-of-art review on MAP of several wastes such as lignocellulosic biomass, waste oils, municipal solid waste and electronic waste, discussing on the biofuels produced (bio-oil, syngas and biochar) as well as the synthesis of carbon nanomaterials (carbon nanotubes and carbon nanofibers). The use of microwave adsorbent and catalyst in MAP process are reviewed, including utilization of biochar as one of the microwave absorbers. Life cycle analysis and scale-up process with the global view of MAP are presented to contribute to the further advancement and commercialization of this technology. Although there are several challenges to be resolved, MAP has a high energy efficiency and is an increasingly feasible technique to be scalable, economical and environmental friendly. [ABSTRACT FROM AUTHOR]