Optimization of Operational Parameters Using Artificial Neural Network and Support Vector Machine for Bio-oil Extracted from Rice Husk.

Bio-oil production from rice husk, an abundant agricultural residue, has gained significant attention as a sustainable and renewable energy source. The current research aims to employ artificial neural network (ANN) and support vector machine (SVM) modeling techniques for the optimization of operating parameters for bio-oil extracted from rice husk ash (RHA) through pyrolysis. ANN and SVM methods are employed to model and optimize the operational conditions, including temperature, heating rate, and feedstock particle size, to enhance the yield and quality of bio-oil. Additionally, ANN modeling is utilized to create a predictive model for bio-oil properties, allowing for the efficient optimization of pyrolysis conditions. This research provides valuable insights into the production and properties of bio-oil from RHA. By harnessing the capabilities of ANN and SVM, this research not only aids in understanding the intricate relationships between process variables and bio-oil properties but also provides a means to systematically enhance the production process. The predictive results obtained from the ANN were found to be good when compared with the SVM. Several models with different numbers of neurons have been trained with different transfer functions. R values for the training, validation, and test phases are around 1.0, i.e., 0.9981, 0.9976, and 0.9978, respectively. The overall R-value was 0.9960 for the proposed network. The findings were considered acceptable, as the overall R-value was close to 1.0. The optimized operational parameters contribute to the efficient conversion of RHA into bio-oil, thereby promoting the use of this sustainable resource for renewable energy production. This approach aligns with the growing emphasis on reducing the environmental impact of traditional fossil fuels and advancing the utilization of alternative and environmentally friendly energy sources.

Optimization of operational parameters using RSM, ANN, and SVM in membrane integrated with rotating biological contactor.

Membrane fouling is a critical bottleneck to the widespread adoption of membrane separation processes. It diminishes the membrane permeability and results in high operational energy costs. The current study presents optimizing the operating parameters of a novel rotating biological contactor (RBC) integrated with an external membrane (RBC + ME) that combines membrane technology with an RBC. In the RBC + ME, the membrane panel is placed external to the bioreactor. Response surface methodology (RSM) is applied to optimize the membrane permeability through three operating parameters (hydraulic retention time (HRT), rotational disk speed, and sludge retention time (SRT)). The artificial neural networks (ANN) and support vector machine (SVM) are implemented to depict the statistical modelling approach using experimental data sets. The results showed that all three operating parameters contribute significantly to the performance of the bioreactor. RSM revealed an optimum value of 40.7 rpm disk rotational speed, 18 h HRT and 12.4 d SRT, respectively. An ANN model with ten hidden layers provides the highest R2 value, while the SVM model with the Bayesian optimizer provides the highest R2. RSM, ANN, and SVM models reveal the highest R-square values of 0.97, 0.99, and 0.99, respectively. Machine learning techniques help predict the model based on the experimental results and training data sets.

Screening of Metal Oxides and Hydroxides for Arsenic Removal from Water Using Molecular Dynamics Simulations.

Arsenic in groundwater is a harmful and hazardous substance that must be removed to protect human health and safety. Adsorption, particularly using metal oxides, is a cost-effective way to treat contaminated water. These metal oxides must be selected systematically to identify the best material and optimal operating conditions for the removal of arsenic from water. Experimental research has been the primary emphasis of prior work, which is time-consuming and costly. The previous simulation studies have been limited to specific adsorbents such as iron oxides. It is necessary to study other metal oxides to determine which ones are the most effective at removing arsenic from water. In this work, a molecular simulation computational framework using molecular dynamics and Monte Carlo simulations was developed to investigate the adsorption of arsenic using various potential metal oxides. The molecular structures have been optimized and proceeded with sorption calculations to observe the adsorption capabilities of metal oxides. In this study, 15 selected metal oxides were screened at a pressure of 100 kPa and a temperature of 298 K for As(V) in the form of HAsO4 at pH 7. Based on adsorption capacity calculations for selected metal oxides/hydroxides, aluminum hydroxide (Al(OH)3), ferric hydroxide (FeOOH), lanthanum hydroxide La(OH)3, and stannic oxide (SnO2) were the most effective adsorbents with adsorption capacities of 197, 73.6, 151, and 42.7 mg/g, respectively, suggesting that metal hydroxides are more effective in treating arsenic-contaminated water than metal oxides. The computational results were comparable with previously published literature with a percentage error of 1%. Additionally, SnO2, which is rather unconventional to be used in this application, demonstrates potential for arsenic removal and could be further explored. The effects of pH from 1 to 13, temperature from 281.15 to 331.15 K, and pressure from 100 to 350 kPa were studied. Results revealed that adsorption capacity decreased for the high-temperature applications while experiencing an increase in pressure-promoted adsorption. Furthermore, response surface methodology (RSM) has been employed to develop a regression model to describe the effect of operating variables on the adsorption capacity of screened adsorbents for arsenic removal. The RSM models utilizing CCD (central composite design) were developed for Al(OH)3, La(OH)3, and FeOOH, having R2 values 0.92, 0.67, and 0.95, respectively, suggesting that the models developed were correct.

SVM and ANN Modelling Approach for the Optimization of Membrane Permeability of a Membrane Rotating Biological Contactor for Wastewater Treatment.

Membrane fouling significantly hinders the widespread application of membrane technology. In the current study, a support vector machine (SVM) and artificial neural networks (ANN) modelling approach was adopted to optimize the membrane permeability in a novel membrane rotating biological contactor (MRBC). The MRBC utilizes the disk rotation mechanism to generate a shear rate at the membrane surface to scour off the foulants. The effect of operational parameters (disk rotational speed, hydraulic retention time (HRT), and sludge retention time (SRT)) was studied on the membrane permeability. ANN and SVM are machine learning algorithms that aim to predict the model based on the trained data sets. The implementation and efficacy of machine learning and statistical approaches have been demonstrated through real-time experimental results. Feed-forward ANN with the back-propagation algorithm and SVN regression models for various kernel functions were trained to augment the membrane permeability. An overall comparison of predictive models for the test data sets reveals the model's significance. ANN modelling with 13 hidden layers gives the highest R2 value of >0.99, and the SVM model with the Bayesian optimizer approach results in R2 values higher than 0.99. The MRBC is a promising substitute for traditional suspended growth processes, which aligns with the stipulations of ecological evolution and environmentally friendly treatment.

Response Surface Methodology and Artificial Neural Network Modelling of Membrane Rotating Biological Contactors for Wastewater Treatment.

Membrane fouling is a major hindrance to widespread wastewater treatment applications. This study optimizes operating parameters in membrane rotating biological contactors (MRBC) for maximized membrane fouling through Response Surface Methodology (RSM) and an Artificial Neural Network (ANN). MRBC is an integrated system, embracing membrane filtration and conventional rotating biological contactor in one individual bioreactor. The filtration performance was optimized by exploiting the three parameters of disk rotational speed, membrane-to-disk gap, and organic loading rate. The results showed that both the RSM and ANN models were in good agreement with the experimental data and the modelled equation. The overall R2 value was 0.9982 for the proposed network using ANN, higher than the RSM value (0.9762). The RSM model demonstrated the optimum operating parameter values of a 44 rpm disk rotational speed, a 1.07 membrane-to-disk gap, and a 10.2 g COD/m2 d organic loading rate. The optimization of process parameters can eliminate unnecessary steps and automate steps in the process to save time, reduce errors and avoid duplicate work. This work demonstrates the effective use of statistical modeling to enhance MRBC system performance to obtain a sustainable and energy-efficient treatment process to prevent human health and the environment.