ANN based modelling of hydrodynamic cavitation processes: Biomass pre-treatment and wastewater treatment

Application of artificial intelligence tools in wastewater and waste gas treatment systems: Recent advances and prospects

The non-linear complex relationships among the process variables in wastewater and waste gas treatment systems possess a significant challenge for real-time systems modelling. Data driven artificial intelligence (AI) tools are increasingly being adopted to predict the process performance, cost-effective process monitoring, and the control of different waste treatment systems, including those involving resource recovery. This review presents an in-depth analysis of the applications of emerging AI tools in physico-chemical and biological processes for the treatment of air pollutants, water and wastewater, and resource recovery processes. Additionally, the successful implementation of AI-controlled wastewater and waste gas treatment systems, along with real-time monitoring at the industrial scale are discussed.

Optimized phenol degradation and lipid production by Rhodosporidium toruloides using response surface methodology and genetic algorithm-optimized artificial neural network

Oleaginous yeast can produce lipids while degrading phenol in wastewater treatment. In this study, a Plackett-Burman Design (PBD) was adopted to identify key factors of phenol degradation and lipid production using R toruloides 9564T. While temperature, inoculum size, and agitation were significant for both the processes (p < 0.05), pH and incubation were significant for lipid production, and phenol removal, respectively. Results from four factors (pH, temperature, inoculum size, and incubation period) central composite design (CCD) experiment were used to formulate quadratic and genetic algorithm-optimized ANN models. The reduced quadratic model for phenol degradation (R2: 0.993) and lipid production (R2: 0.958) were marginally inferior to ANN models (R2: 0.999, 0.982, respectively) on training sets. Multi-objective optimization with equal importance suggests phenol degradation between 106.4 and 108.76%, and lipid production of 0.864-0.903 g/L, by polynomial and ANN models. Complete phenol degradation (100%) and 3.35-fold increment (0.918 g/L) in lipid production were obtained at pH 6.07, inoculum size 14.68% v/v, at 29.5 °C in 92.17 h experimentally.

Bridging artificial intelligence and fucoxanthin for the recovery and quantification from microalgae

Fucoxanthin is a carotenoid that possesses various beneficial medicinal properties for human well-being. However, the current extraction technologies and quantification techniques are still lacking in terms of cost validation, high energy consumption, long extraction time, and low yield production. To date, artificial intelligence (AI) models can assist and improvise the bottleneck of fucoxanthin extraction and quantification process by establishing new technologies and processes which involve big data, digitalization, and automation for efficiency fucoxanthin production. This review highlights the application of AI models such as artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS), capable of learning patterns and relationships from large datasets, capturing non-linearity, and predicting optimal conditions that significantly impact the fucoxanthin extraction yield. On top of that, combining metaheuristic algorithm such as genetic algorithm (GA) can further improve the parameter space and discovery of optimal conditions of ANN and ANFIS models, which results in high R2 accuracy ranging from 98.28% to 99.60% after optimization. Besides, AI models such as support vector machine (SVM), convolutional neural networks (CNNs), and ANN have been leveraged for the quantification of fucoxanthin, either computer vision based on color space of images or regression analysis based on statistical data. The findings are reliable when modeling for the concentration of pigments with high R2 accuracy ranging from 66.0% - 99.2%. This review paper has reviewed the feasibility and potential of AI for the extraction and quantification purposes, which can reduce the cost, accelerate the fucoxanthin yields, and development of fucoxanthin-based products.

ANN based surrogate model for key Physico-chemical effects of cavitation

Intense and localised physico-chemical effects realised by cavitation such as generation of hydroxyl radicals, high-speed jets, and very high energy dissipation rates are being harnessed for a wide range of applications from emulsions, crystallisation, reactions to water treatment and waste valorisation. Single cavity models are typically used to quantitatively estimate such localised effects of cavity collapse. However, these models demand significant computing resources for resolving fast dynamics and therefore are very difficult, if not impossible, to integrate with CFD based cavitation device or reactor scale models. This severely limits the utility of device/ reactor scale models in simulating key applications of interest. In this work, we present, for the first time, artificial neural network (ANN) based surrogate models which accurately represent complex physico-chemical effects of cavity collapse. Recently developed cavity dynamics model was used for generating training data set encompassing both acoustic and hydrodynamic cavitation. Appropriate methodology for training ANN was developed. A shallow three hidden layer dense ANN was found to be more effective for estimating three main effects of cavity collapse: jet velocity, •OH generation and localised energy dissipation rate. The performance of trained ANN was then evaluated by comparing the predictions with the totally unseen data obtained from the cavity dynamics model. The developed ANN was shown to simulate unseen data very well not just within the range of training data (interpolation) but also beyond (extrapolation). Algebraic equations representing ANN are included to facilitate incorporation in device/ reactor scale CFD models. The presented methodology and results will be useful for developing high-fidelity CFD models of cavitation devices/ reactors based on key physico-chemical effects of cavity collapse.

Using neural networks and a fuzzy inference system to evaluate the risk of wildfires and the pinpointing of firefighting stations in forests on the northern slopes of the Zagros Mountains, Iran (case study: Shimbar national wildlife preserve)

Predicting potential fire hazard zones in natural areas is one of the means of mitigating and managing fires. The current research focuses on the prioritizing of elements which contribute to the spread of fire and the special zoning of potentially dangerous areas in addition to the pinpointing of locations for the establishment of fire stations in forested areas in the Shimbar national reserve based on historical data spanning 2001 to 2018. The study utilizes elements (physiological, vegetation cover, meteorological, anthropological factors) contributing to wildfires as inputs into an artificial neural network and the development of a fuzzy inference system in order to produce fire zoning maps for the region under study. The map is divided into five sectors, i.e., minimum, low, moderate, high, and maximum risk of fire. The validation of the fire zoning map was evaluated at 0.83 and the RMSE error was 0.75. The results obtained show that 20% of the area under study is within the average risk category, 11% is within the high-risk category, and 10% is within the very high-risk category of a potential fire hazard. The most important variables were distance from a flowing source, i.e., river or stream, the land formation type, elevation, and the minimum temperature. The identification of suitable locations for firefighting stations was carried out by merging the fuzzy inference system model and Arc GIS, and the results obtained defined 16 possible locations. It was concluded that the application of hybrid models when dealing with the aforementioned variables is effective when seeking to determine locations for the establishment of firefighting stations and rural safety services; moreover, such hybrid models are highly efficacious for determining of fire hazard zones. It is proposed that hybrid models be applied on a large scale for the prevention, control, and management of fires throughout the country.

Modeling of Hydrodynamic Cavitation Reactors: Reflections on Present Status and Path Forward

Hydrodynamic cavitation (HC) is finding ever increasing applications in water, energy, chemicals, and materials sectors. HC generates intense shear, localized hot spots, and hydroxyl radicals, which are harnessed for realizing desired physicochemical transformations. Despite identification of HC as one of the most promising technology platforms, its potential is not yet adequately translated in practice. Lack of appropriate models for design, optimization, and scale-up of HC reactors is one of the primary reasons for this. In this work, the current status of modeling of HC reactors is presented. Various prevailing approaches covering empirical, phenomenological, and multiscale models are critically reviewed in light of personal experience of their application. Use of these approaches for different applications such as biomass pretreatment and wastewater treatment is briefly discussed. Some comments on extending these models for other applications like emulsions and crystallization are included. The presented models and discussion will be useful for practicing engineers and scientists interested in applying HC for a variety of applications. Some thoughts on further advances in modeling of HC reactors and outlook are shared, which may stimulate further research on improving the fidelity of computational models of HC reactors.

Using CFD simulations to investigate the shear stress in hydrodynamic cavitation reactors coupled with experimental validation using colony count measurements

The current work investigates the shear stress distribution in hydrodynamic cavitation reactors with two different geometries using CFD simulations. Venturi type (positive geometry) and bore (negative geometry) were used to induce cavitation. Experimental validation of the predictions from simulations was also conducted by calculating the reduction rate in the colony count of Legionella pneumophila, a pathogenic bacterial strain. Both the numerical and experimental studies revealed the significant influence of the shape of the cavitation-inducing geometry on the flow characteristics and the distribution of shear stress. The simulation data indicated high shear stress formation in the positive geometry as a venturi, with the cavitation ranges for the two reactors being far apart from each other. The experimental study also confirmed that the flow conditions in the venturi-type reactor were more favourable compared to the bore geometry, resulting in a bacterial reduction efficiency as high as 99.98%. It was clearly demonstrated that the geometry of the cavitating device plays a crucial role in deciding the shear stress and its efficacy for the desired applications as per the predictions of the simulation model validated by the experimental results.

Methods for Intensifying Biogas Production from Waste: A Scientometric Review of Cavitation and Electrolysis Treatments

This article presents future trends in research using microbiological methods to intensify bioprocesses for biogas production. The pretreatment by combinations of physical and chemical methods, such as cavitation and electrolysis, is considered. The approach of the article involved reviewing the residual area on the intensification technologies of anaerobic digestion with current methods to improve the quality and quantity of biogas. The most valuable reported positive results of the pretreatment of biological raw materials in the cavitation process were reviewed and are presented here. A model of the effect of electrolysis on the species diversity of bacteria in anaerobic digestion was developed, and changes in the dominance of the ecological and trophic systems were revealed on the basis of previous studies. The stimulating effect on biogas yield, reduction in the stabilization period of the reactor, and inactivation of microorganisms at lower temperatures is associated with different pretreatment methods that intensify anaerobic digestion. More research is recommended to focus on the electrolysis treatment of different types of waste and their ratios with optimization of regime parameters, as well as in combination with other pretreatments to produce biomethane and biohydrogen in larger quantities and in better qualities.

Application of machine learning in anaerobic digestion: Perspectives and challenges

Anaerobic digestion (AD) is widely adopted for remediating diverse organic wastes with simultaneous production of renewable energy and nutrient-rich digestate. AD process, however, suffers from instability, thereby adversely affecting biogas production. There have been significant efforts in developing strategies to control the AD process to maintain process stability and predict AD performance. Among these strategies, machine learning (ML) has gained significant interest in recent years in AD process optimization, prediction of uncertain parameters, detection of perturbations, and real-time monitoring. ML uses inductive inference to generalize correlations between input and output data, subsequently used to make informed decisions in new circumstances. This review aims to critically examine ML as applied to the AD process and provides an in-depth assessment of important algorithms (ANN, ANFIS, SVM, RF, GA, and PSO) and their applications in AD modeling. The review also outlines some challenges and perspectives of ML, and highlights future research directions.

Degradation of Bisphenol A by ozonation in a rotating packed bed: Modeling by response surface methodology and artificial neural network

The ozonation process of Bisphenol A (BPA) in a rotating packed bed (RPB) was modeled by response surface methodology (RSM) and artificial neural network (ANN). Experiments were performed according to the Box-Behnken design, and the interactive effects of various parameters including ozone concentration, pH, rotation speed of RPB and liquid flow rate on BPA degradation efficiency were investigated. Ozone concentration and pH had the most significant interactive effects on BPA degradation efficiency while rotation speed of RPB had no significant interactive effects with other variables. A second order polynomial equation was obtained to predict BPA degradation efficiency. Also, a multi-layered feed-forward ANN model was constructed based on the data of RSM experiments. Six neurons in hidden layer had the highest correlation coefficient (R_ANN = 0.99158). A comparison between RSM and ANN models suggested that both can accurately predict BPA degradation efficiency (R_RSM = 0.99559). The highest BPA degradation efficiency (99.52 %) was achieved under the conditions of ozone concentration of 20 mg L^-1, pH of 11, liquid flow rate of 10 L h^-1 and rotation speed of RPB of 800 rpm, which was well predicted by the RSM model (99.54 %) and the ANN model (99.82 %). However, the RSM model was slightly better than the ANN model owing to its higher determination coefficient (R²_RSM = 0.9912, R²_ANN = 0.9827) and lower mean square error (MSE_RSM = 0.0001684, MSE_ANN = 0.0003305).

Novel strategies to enhance hydrodynamic cavitation in a circular venturi using RANS numerical simulations

Hydrodynamic cavitation is a popular advanced oxidation technique and it has received wide range of applications from waste water treatment to the nanoparticles synthesis in recent years. The enhancement of the intensity of the hydrodynamic cavitation is always been an emerging field of research. Within this framework, we have proposed and investigated three distinct strategies to enhance the intensity of cavitation in a circular venturi, namely, (1) by introducing the surface roughness on the wall (2) single or multiple circular hurdles in the diverging section (3) By modifying the diverging section from planer to the trumpet shape. RANS (Reynolds Averaged Navier-Stokes) based numerical simulations are carried out the over wide range of conditions: 2≤PR≤6 (pressure ratio), 6.2^∘≤β≤10^∘ (half divergent angle), 15^∘≤α≤20^∘ (half convergent angle), and 1≤l/d≤3 (throat length). An extensive numerical and experimental validation with the literature have been presented to ensure the reliability and accuracy of present work. Detailed results on velocity fields, local and average volume fraction, pressure loss coefficients, cavitation number, discharge coefficient and pressure distribution are reported as function of dimensionless parameters. Five designs of various combinations of surface roughness, circular hurdles, and trumpet diverging section have been compared. The effect of surface roughness on trumpet diverging wall has been observed to be more pronounced than the other designs. Trumpet diverging wall with surface roughness is found to be optimum for the practical applications.

Effect of hydrodynamic cavitation water treatment on Pseudomonas aeruginosa quorum-sensing molecules

Hydrodynamic cavitation treatment was used for the functional inactivation of quorum-sensing lactone molecules of Pseudomonas aeruginosa. Hydroxyl radicals formed as well as the shear effects during the cavitation process induced the inactivation of the signal molecules through hydrolysis reaction coupled with bacterial destruction. Concentration of two different types of homoserine lactones (HSL) molecules was tested after the treatment at various rotational speeds. It was found that the strongest effects can be achieved at speeds > 2000 rpm. This value is considered as an onset speed of dominant cavitation, and it is in agreement with literature data. The experimental trends were in agreement with the calculations based on the finite element modelling, which show a significant increase in average shear stress at higher rotational speeds. Overall, the work has demonstrated the possible effects of hydrodynamic cavitation on the quorum-sensing molecules of Pseudomonas aeruginosa for the first time.