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.

Nanofiltration PVP Membrane with Gradient Cross-Linking for Efficient Polycyclic Aromatic Hydrocarbons Removal.

Polycyclic aromatic hydrocarbons (PAHs) are persistent organic pollutants that may contaminate various water sources and pose serious dangers to human health and the environment. Due to their capacity for size-based separation, nanofiltration membranes have become efficient instruments for PAH removal. However, issues such as membrane fouling and ineffective rejection still exist. To improve PAH rejection while reducing fouling problems, this work created a new gradient cross-linking poly(vinylpyrrolidone) (PVP) nanofiltration membrane. The gradient cross-linking technique enhanced the rejection performance and antifouling characteristics of the membrane. The results demonstrated that the highest membrane flow was achieved at a 0.15% SDS-PVP membrane. There is a trade-off between membrane flux and salt rejection since salt rejection increases with SDS owing to the growth of big pores. The membrane flux was reduced for the 0.25% SDS-PVP membrane owing to poor SDS dispersion. The prepared membrane showed enhanced removal efficiencies for the removal of the PAH compounds. The PVP membrane has the potential to be used in several water treatment applications, improving water quality, and preserving the environment.

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.

Optimization of syngas production from co-gasification of palm oil decanter cake and alum sludge: An RSM approach with char characterization.

The study explores co-gasification of palm oil decanter cake and alum sludge, investigating the correlation between input variables and syngas production. Operating variables, including temperature (700-900 °C), air flow rate (10-30 mL/min), and particle size (0.25-2 mm), were optimized to maximize syngas production using air as the gasification agent in a fixed bed horizontal tube furnace reactor. Response Surface Methodology with the Box-Behnken design was used employed for optimization. Fourier Transformed Infra-Red (FTIR) and Field Emission Scanning Electron Microscopic (FESEM) analyses were used to analyze the char residue. The results showed that temperature and particle size have positive effects, while air flow rate has a negative effect on the syngas yield. The optimal CO + H2 composition of 39.48 vol% was achieved at 900 °C, 10 mL/min air flow rate, and 2 mm particle size. FTIR analysis confirmed the absence of C─Cl bonds and the emergence of Si─O bonds in the optimized char residue, distinguishing it from the raw sample. FESEM analysis revealed a rich porous structure in the optimized char residue, with the presence of calcium carbonate (CaCO3) and aluminosilicates. These findings provide valuable insights for sustainable energy production from biomass wastes.

Effect of Operating Parameters on the Performance of Integrated Fixed-Film Activated Sludge for Wastewater Treatment.

Integrated fixed-film activated sludge (IFAS) is a hybrid wastewater treatment process that combines suspended and attached growth. The current review provides an overview of the effect of operating parameters on the performance of IFAS and their implications for wastewater treatment. The operating parameters examined include hydraulic retention time (HRT), solids retention time (SRT), dissolved oxygen (DO) levels, temperature, nutrient loading rates, and aeration. Proper control and optimization of these parameters significantly enhance the treatment efficiency and pollutant removal. Longer HRT and appropriate SRT contribute to improved organic matter and nutrient removal. DO levels promote the growth of aerobic microorganisms, leading to enhanced organic matter degradation. Temperature influences microbial activity and enzymatic reactions, impacting treatment efficiency. Nutrient loading rates must be carefully managed to avoid system overload or inhibition. Effective aeration ensures uniform distribution of wastewater and biofilm carriers, optimizing contact between microorganisms and pollutants. IFAS has been used in water reuse applications, providing a sustainable and reliable water source for non-potable uses. Overall, IFAS has proven to be an effective and efficient treatment process that can provide high-quality effluent suitable for discharge or reuse. Understanding the effects of these operating parameters helps to optimize the design and operation for efficient wastewater treatment. Further research is needed to explore the interactions between different parameters, evaluate their impact under varying wastewater characteristics, and develop advanced control strategies for improved performance and sustainability.

Response Surface Methodology for the Synthesis and Characterization of Bio-Oil Extracted from Biomass Waste and Upgradation Using the Rice Husk Ash Catalyst.

Rice husk ash (RHA), a low-cost biomaterial, was utilized to form bio-oil from pyrolysis in a batch-stirred reactor, followed by its upgradation using the RHA catalyst. In the present study, the effect of temperature (ranging from 400 to 480 °C) on bio-oil production produced from RHA was studied to obtain the maximum bio-oil yield. Response surface methodology (RSM) was applied to investigate the effect of operational parameters (temperature, heating rate, and particle size) on the bio-oil yield. The results showed that a maximum bio-oil output of 20.33% was obtained at 480 °C temperature, 80 °C/min heating rate, and 200 µm particle size. Temperature and heating rate positively impact the bio-oil yield, while particle size has little effect. The overall R2 value of 0.9614 for the proposed model proved in good agreement with the experimental data. The physical properties of raw bio-oil were determined, and 1030 kg/m3 density, 12 MJ/kg calorific value, 1.40 cSt viscosity, 3 pH, and 72 mg KOH/g acid value were obtained, respectively. To enhance the characteristics of the bio-oil, upgradation was performed using the RHA catalyst through the esterification process. The upgraded bio-oil stemmed from a density of 0.98 g/cm3, an acid value of 58 mg of KOH/g, a calorific value of 16 MJ/kg, and a viscosity 10.5 cSt, respectively. The physical properties, GC-MS and FTIR, showed an improvement in the bio-oil characterization. The findings of this study indicate that RHA can be used as an alternative source for bio-oil production to create a more sustainable and cleaner environment.

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.

Response Surface Methodology for Optimization of Rotating Biological Contactor Combined with External Membrane Filtration for Wastewater Treatment.

A large amount of wastewater is directly discharged into water bodies without treatment, causing surface water contamination. A rotating biological contactor (RBC) is an attached biological wastewater treatment process that offers a low energy footprint. However, its unstable removal efficiency makes it less popular. This study optimized operating parameters in RBC combined with external membrane filtration (RBC-ME), in which the latter acted as a post-treatment step to stabilize the biological performance. Response surface methodology (RSM) was employed to optimize the biological and filtration performance by exploiting three parameters, namely disk rotation, hydraulic retention time (HRT), and sludge retention time (SRT). Results show that the RBC-ME exhibited superior biological treatment capacity and higher effluent quality compared to stand-alone RBC. It attained 87.9 ± 3.2% of chemical oxygen demand, 45.2 ± 0.7% total nitrogen, 97.9 ± 0.1% turbidity, and 98.9 ± 1.1% ammonia removals. The RSM showed a good agreement between the model and the experimental data. The maximum permeability of 144.6 L/m2 h bar could be achieved under the optimum parameters of 36.1 rpm disk rotation, 18 h HRT, and 14.9 d SRT. This work demonstrated the effective use of statistical modeling to enhance RBC-ME system performance to obtain a sustainable and energy-efficient condition.

Recent progress in integrated fixed-film activated sludge process for wastewater treatment: A review.

Integrated fixed-film activated sludge (IFAS) process is considered as one of the leading-edge processes that provides a sustainable solution for wastewater treatment. IFAS was introduced as an advancement of the moving bed biofilm reactor by integrating the attached and the suspended growth systems. IFAS offers advantages over the conventional activated sludge process such as reduced footprint, enhanced nutrient removal, complete nitrification, longer solids retention time and better removal of anthropogenic composites. IFAS has been recognized as an attractive option as stated from the results of many pilot and full scales studies. Generally, IFAS achieves >90% removals for combined chemical oxygen demand and ammonia, improves sludge settling properties and enhances operational stability. Recently developed IFAS reactors incorporate frameworks for either methane production, energy generation through algae, or microbial fuel cells. This review details the recent development in IFAS with the focus on the pilot and full-scale applications. The microbial community analyses of IFAS biofilm and floc are underlined along with the special emphasis on organics and nitrogen removals, as well as the future research perspectives.