Prediction the performance of multistage moving bed biological process using artificial neural network (ANN)

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.

Explainable machine learning-driven predictive performance and process parameter optimization for caproic acid production

In this study, four machine learning (ML) prediction models were developed to predict and optimize the production performance of caproic acid based on substrates, products, and process parameters. The XGBoost outperformed others, with a high R2 of 0.998 on the training set and 0.885 on the test set. Feature importance analysis revealed hydraulic retention time (HRT) and butyric acid concentration are decisive. The SHAP method offered profound insights into the interplay and cumulative effects of substrate composition, identified the synergistic effects between butyric acid and lactic acid, and emphasized adding glucose can benefit caproic with lactic acid co-fermentation. By integrating the Adaptive Variation Particle Swarm Optimization (AVPSO) algorithm, the optimal process conditions to achieve a maximum caproic acid production of 8.64 g/L was obtained. This study not only advances caproic acid production but contributes a versatile ML-driven strategy applicable to bioprocess optimizations, potentially transformative for sustainable and economically viable bioproduction.

Intelligent Gas Detection: g-C3N4/Polypyrrole Decorated Alginate Paper as Smart Selective NH3/NO2 Sensors at Room Temperature

Chemiresistive NH3/NO2 sensors are attracting considerable attention for use in air-conditioning systems. However, the existing sensors suffer from cross-sensitivity, detection limit, and power consumption, owing to the inadequate charge-transfer ability of gas-sensing materials. Herein, we develop a flexible NH3/NO2 sensor based on graphitic carbon nitride/polypyrrole decorated alginate paper (AP@g-CN/PPy). The flexible sensor can work at room temperature and exhibits a positive response of 23-246% and a negative response of 37-262% toward 0.1-5 ppm of NH3 and NO2, which is ∼4.5 times and ∼7.0 times higher than a pristine PPy sensor. Moreover, the sensor exhibits flexibility, reproducibility, long-term stability, anti-interference, and high resilience to humidity, indicating its promising potential in real applications. Using the 9 feature parameters extracted from the transient response, a matched deep learning model was developed to achieve qualitative recognition of different types of gases with distinguished decision boundaries. This work not only provides an alternative gas-sensing material for dual NH3/NO2 sensing but also establishes an intelligent strategy to identify hazardous gases under an interfering atmosphere.

Comparative Analysis of Bacterial Information of Biofilms and Activated Sludge in Full-Scale MBBR-IFAS Systems

This study extensively analyzed the bacterial information of biofilms and activated sludge in oxic reactors of full-scale moving bed biofilm reactor-integrated fixed-film activated sludge (MBBR-IFAS) systems. The bacterial communities of biofilms and activated sludge differed statistically (R = 0.624, p < 0.01). The denitrifying genera Ignavibacterium, Phaeodactylibacter, Terrimonas, and Arcobacter were more abundant in activated sludge (p < 0.05), while comammox Nitrospira was more abundant in biofilms (p < 0.05), with an average relative abundance of 8.13%. Nitrospira and Nitrosomonas had weak co-occurrence relationships with other genera in the MBBR-IFAS systems. Potential function analysis revealed no differences in pathways at levels 1 and 2 based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) between biofilms and activated sludge. However, in terms of pathways at level 3, biofilms had more potential in 26 pathways, including various organic biodegradation and membrane and signal transportation pathways. In comparison, activated sludge had more potential in only five pathways, including glycan biosynthesis and metabolism. With respect to nitrogen metabolism, biofilms had greater potential for nitrification (ammonia oxidation) (M00528), and complete nitrification (comammox) (M00804) concretely accounted for methane/ammonia monooxygenase (K10944, K10945, and K10946) and hydroxylamine dehydrogenase (K10535). This study provides a theoretical basis for MBBR-IFAS systems from the perspective of microorganisms.

Predicting effluent quality parameters for wastewater treatment plant: A machine learning-based methodology

Wastewater Treatment Plants (WWTPs) present complex biochemical processes of high variability and difficult prediction. This study presents an innovative approach using Machine Learning (ML) models to predict wastewater quality parameters. In particular, the models are applied to datasets from both a simulated wastewater treatment plant (WWTP), using DHI WEST software (WEST WWTP), and a real-world WWTP database from Santa Catarina Brewery AMBEV, located in Lages/SC - Brazil (AMBEV WWTP). A distinctive aspect is the evaluation of predictive performance in continuous data scenarios and the impact of changes in WWTP operations on predictive model performance, including changes in plant layout. For both plants, three different scenarios were addressed, and the quality of predictions by random forest (RF), support vector machine (SVM), and multilayer perceptron (MLP) models were evaluated. The prediction quality by the MLP model reached an R2 of 0.72 for TN prediction in the WEST WWTP output, and the RF model better adapted to the real data of the AMBEV WWTP, despite the significant discrepancy observed between the real and the predicted data. Techniques such as Partial Dependence Plots (PDP) and Permutation Importance (PI) were used to assess the importance of features, particularly in the simulated WEST tool scenario, showing a strong correlation of prediction results with influent parameters related to nitrogen content. The results of this study highlight the importance of collecting and storing high-quality data and the need for information on changes in WWTP operation for predictive model performance. These contributions advance the understanding of predictive modeling for wastewater quality and provide valuable insights for future practice in wastewater treatment.

Innovative approach for predicting biogas production from large-scale anaerobic digester using long-short term memory (LSTM) coupled with genetic algorithm (GA)

An artificial neural network (ANN) model called long-short term memory (LSTM), coupled with a genetic algorithm (GA) for feature selection, was used to predict biogas production of large-scale anaerobic digesters (ADs) of Tehran South Wastewater Treatment Plant (Iran), with a biogas production of approximately 30,000 Nm3/d. In order to employ the real conditions, the hydraulic retention time (HRT) of the ADs (21 days) was considered as the LSTM look-back window. To evaluate the model predictions, three different scenarios were defined. In the first scenario, the model predicted the produced biogas by using raw wastewater characteristics and reached the coefficient of determination of R2 = 0.84. The GA selected four out of eleven parameters of raw wastewater, including loads of BOD5, COD, TSS, and TN (kg/d), as the most informative data for the model. In the second scenario, the model predicted the produced biogas by employing the data of the thickened sludge streams entering the ADs and yielded a higher accuracy (R2 = 0.89). In this scenario, GA selected two out of six parameters of the sludge streams, including total flow rate (m3/d) and average solids content (w/w%). Finally, in the third scenario, by putting the parameters of the two previous scenarios together, the model's prediction accuracy increased slightly (R2 = 0.90). The results demonstrated that the GA-LSTM modeling technique could achieve reliable performance in predicting biogas production of large-scale ADs by including HRT in modeling procedure. It was also found that the raw wastewater characteristics severely affect AD behavior and can be successfully used as the input data of the AD models.

Artificial intelligence-enabled optimization of Fe/Zn@biochar photocatalyst for 2,6-dichlorophenol removal from petrochemical wastewater: A techno-economic perspective

While numerous studies have addressed the photocatalytic degradation of 2,6-dichlorophenol (2,6-DCP) in wastewater, an existing research gap pertains to operational factors' optimization by non-linear prediction models to ensure a cost-effective and sustainable process. Herein, we focus on optimizing the photocatalytic degradation of 2,6-DCP using artificial intelligence modeling, aiming at minimizing initial capital outlay and ongoing operational expenses. Hence, Fe/Zn@biochar, a novel material, was synthesized, characterized, and applied to harness the dual capabilities of 2,6-DCP adsorption and degradation. Fe/Zn@biochar exhibited an adsorption energy of -21.858 kJ/mol, effectively capturing the 2,6-DCP molecules. This catalyst accumulated photo-excited electrons, which, upon interaction with adsorbed oxygen and/or dissolved oxygen generated •O2-. The •OH radicals could also be produced from h+ in the Fe/Zn@biochar valence band, cleaving the C-Cl bonds to Cl- ions, dechlorinated byproducts, and phenols. An artificial neural network (ANN) model, with a 4-10-1 topology, "trainlm" training function, and feed-forward back-propagation algorithm, was developed to predict the 2,6-DCP removal efficiency. The ANN prediction accuracy was expressed as R2 = 0.967 and mean squared error = 5.56e-22. The ANN-based optimized condition depicted that over 90% of 2,6-DCP could be eliminated under C0 = 130 mg/L, pH = 2.74, and catalyst dosage = 168 mg/L within ∼4 h. This optimum condition corresponded to a total cost of $7.70/m3, which was cheaper than the price estimated from the unoptimized photocatalytic system by 16%. Hence, the proposed ANN could be employed to enhance the 2,6-DCP photocatalytic degradation process with reduced operational expenses, providing practical and cost-effective solutions for petrochemical wastewater treatment.

Quantitative prediction of the hydraulic performance of free water surface constructed wetlands by integrating numerical simulation and machine learning

Quantitative prediction of the design parameter-influenced hydraulic performance is significant for optimizing free water surface constructed wetlands (FWS CWs) to reduce point and non-point source pollution and improve land utilization. However, owing to limitations of the test conditions and data scale, a quantitative prediction model of the hydraulic performance under multiple design parameters has not yet been established. In this study, we integrated field test data, mechanism model, statistical regression, and machine learning (ML) to construct such quantitative prediction models. A FWS CW numerical model was established by integrating 13 groups of trace data from field tests. Subsequently, training, test and extension datasets comprising 125 (5^3), 25 (L₂₅(5⁶)) and 16 (L₁₆(4⁴)) data points, respectively, were generated via numerical simulation of multi-level value combination of three quantitative design parameters, namely, water depth, hydraulic loading rate (HLR), and aspect ratio. The short circuit index (φ₁₀), Morrill dispersion index (MDI), hydraulic efficiency (λ) and moment index (MI) were used as representative hydraulic performance indicators. Training set with large samples were analyzed to determine the variation rules of different hydraulic indicators. Based on the control variable method, φ₁₀, λ, and MI grew exponentially with increasing aspect ratio whereas MDI showed a decreasing trend; with increasing water depth, φ₁₀, λ, and MI showed polynomial decreases whereas MDI increased; with increasing HLR, φ₁₀, λ, and MI slowly increased linearly whereas MDI showed the opposite trend. Finally, we constructed models based on multivariate nonlinear regression (MNLR) and ML (random forest (RF), multilayer perceptron (MLP), and support vector regression. The coefficients of determination (R²) of the MNLR and ML models fitting the training and test sets were all greater than 0.9; however, the generalization abilities of different models in the extension set were different. The most robust MLP, MNLR without interaction term, and RF models were recommended as the preferred models to hydraulic performance prediction. The extreme importance of aspect ratio in hydraulic performance was revealed. Thus, gaps in the current understanding of multivariate quantitative prediction of the hydraulic performance of FWS CWs are addressed while providing an avenue for researching FWS CWs in different regions according to local conditions.

Optimization of phosphorus recovery using electrochemical struvite precipitation and comparison with iron electrocoagulation system

A batch monopolar reactor was developed for total phosphorus (TP) recovery using electrochemical struvite precipitation. This study involves the optimization of factors using response surface methodology to maximize the TP recovery. The optimal parameters for this study were found to be a pH of 8.40, a retention time of 35 min, a current density of 300 A/m² , and an interelectrode distance of 0.5 cm, resulting in 97.3% of TP recovery and energy consumption of 2.35 kWh/m³ . A kinetic study for TP removal revealed that at optimum operating conditions, TP removal follows second-order kinetics (removal rate constant(K) = 0.0117 mg/(m² ·min)). The system performance was compared to the performance of an iron electrocoagulation system. The composition of the precipitate obtained during the optimal runs were analyzed using X-ray diffraction and EDS analysis. X-ray diffraction analysis of the magnesium precipitate revealed the presence of struvite as the only crystalline compound. PRACTITIONER POINTS: Electrochemical struvite precipitation has the potential to recover total phosphorus from anaerobic bioreactor effluent. Optimum conditions for phosphorus recovery was found at a pH of 8.4, retention time of 35 min, current density of 300 A/m2, and interelectrode distance of 0.5 cm. The quadratic model predicted complete (100 %) TP recovery under optimized conditions, whereas 97.3 % recovery was observed under experimental conditions. TP removal under optimum conditions followed second-order rate equation (removal rate constant(K) = 0.0117 mg/(m² ·min)). XRD analysis of the precipitate revealed struvite as the only crystalline compound.

Elucidating the performance of integrated anoxic/oxic moving bed biofilm reactor: Assessment of organics and nutrients removal and optimization using feed forward back propagation neural network

A lab-scale integrated anoxic and oxic (A/O) moving bed biofilm reactor (MBBR) was investigated for the removal of organics and nutrients by varying chemical oxygen demand (COD) to NH₄-N ratio (C/N ratio: 3.5, 6.75, and 10), hydraulic retention time (HRT: 6 h, 15 h, and 24 h), and recirculation ratio (R: 1, 2, and 3). The use of activated carbon coated carriers prepared from waste polyethylene material and polyurethane sponges attached to a cylindrical frame in the integrated A/O MBBR increased the attached growth biomass significantly. >95 % of COD removal was observed under the C/N ratio of 10 at an HRT of 24 h. While the low C/N ratio favored the removal of NH₄-N (∼98 %) and PO₄^3--P (∼90 %) with an optimal R of 1.75. Using the experimental dataset, to predict and forecast the performance of integrated A/O MBBR, a feed-forward-backpropagation-neural-network model was developed.

Artificial intelligence and machine learning-based monitoring and design of biological wastewater treatment systems

Artificial intelligence (AI) and machine learning (ML) are currently used in several areas. The applications of AI and ML based models are also reported for monitoring and design of biological wastewater treatment systems (WWTS). The available information is reviewed and presented in terms of bibliometric analysis, model's description, specific applications, and major findings for investigated WWTS. Among the applied models, artificial neural network (ANN), fuzzy logic (FL) algorithms, random forest (RF), and long short-term memory (LSTM) were predominantly used in the biological wastewater treatment. These models are tested by predictive control of effluent parameters such as biological oxygen demand (BOD), chemical oxygen demand (COD), nutrient parameters, solids, and metallic substances. Following model performance indicators were mainly used for the accuracy analysis in most of the studies: root mean squared error (RMSE), mean square error (MSE), and determination coefficient (DC). Besides, outcomes of various models are also summarized in this study.

Metagenomic insights into microbial nitrogen metabolism in two-stage anoxic/oxic-moving bed biofilm reactor system with multiple chambers for municipal wastewater treatment

To explore the microbial nitrogen metabolism of a two-stage anoxic/oxic (A/O)-moving bed biofilm reactor (MBBR), biofilms of the system's chambers were analyzed using metagenomic sequencing. Significant differences in microbial populations were found among the pre-anoxic, oxic and post-anoxic MBBRs (P < 0.01). Nitrospira and Nitrosomonas had positive correlations with ammonia nitrogen (NH₄⁺-N) removal, and were also predominant in oxic MBBRs. These organisms were the hosts of functional genes for nitrification. The denitrifying genera were predominant in anoxic MBBRs, including Thiobacillus and Sulfurisoma in pre-anoxic MBBRs and Dechloromonas and Thauera in post-anoxic MBBRs. The four genera had positive correlations with total nitrate and nitrite nitrogen (NO_X^--N) removal and were the hosts of functional genes for denitrification. Specific functional biofilms with different microbial nitrogen metabolisms were formed in each chamber of this system. This work provides a microbial theoretical support for the two-stage A/O-MBBR system.

Performance and bacterial community analysis of a two-stage A/O-MBBR system with multiple chambers for biological nitrogen removal

A two-stage anoxic/oxic (A/O)-moving bed biofilm reactor (MBBR) system with multiple chambers was established for municipal wastewater treatment. At the total hydraulic retention time (HRT) of 11.2 h and nitrate recycling ratio of 1, the removal efficiencies reached 83.8%, 82.5%, and 77.8% for soluble chemical oxygen demand (SCOD), 98.0%, 97.5%, and 94.9% for ammonia nitrogen (NH₄⁺-N), and 91.8%, 92.0%, and 87.7% for total inorganic nitrogen (TIN) in summer, autumn and winter, respectively. Biofilms with functional bacterial populations were formed in the pre-anoxic reactors, the pre-oxic reactors, the post-anoxic reactors and the post-oxic reactors of the two-stage A/O-MBBR system. The highest nitrification potential was found in the last oxic reactor of the first A/O-MBBR subsystem with the highest relative abundances of the functional genes including [EC:1.14.99.39] and [EC:1.7.2.6]). The highest denitrification potential was found in the post-anoxic reactors with the highest relative abundances of the functional genes including [EC:1.7.2.1], [EC:1.7.2.5] and [EC:1.7.2.4]. This work constructed an efficient municipal biological nitrogen removal technology to achieve high effluent nitrogen standards in winter, and investigated its working mechanism to provide a basis for its design and optimization.

Synergistic effect of chitosan-based sludge aggregates CS@NGS inoculum accelerated the start-up of biofilm reactor treating aquaculture effluent: Insights into performance, microbial characteristics, and functional genes

The moving bed bioreactor (MBBR) process has drawn more attention as a promising biological wastewater treatment process. Nevertheless, achieving quick start-up and microbial biofilm formation remains a significant challenge. Consequently, the present study investigated a novel chitosan-based natural sludge (CS@NGS) seeding strategy for the accelerated start-up of MBBR. Three identical bioreactors were employed; the first bioreactor was without sludge seed as the control (BR1), the second was inoculated only with sludge (BR2), and the third was inoculated with CS@NGS according to the proposed seeding method (BR3). All bioreactors were utilised to treat simulated recirculating aquaculture systems (RAS) effluent. Resultantly, the CS@NGS shortened the start-up period from over twenty to seven days due to the enhanced initial microbial adhesion and biofilm formation. Under optimal conditions, the ammonium removal in BR3 approached 100%, which was relatively higher than BR2 (96.35 ± 1.12%) and BR1 (92.56 ± 2.17%). Moreover, a low nitrite accumulation was exhibited in the effluents, approximately ≤0.03 mg L^-1. The process performance correlated positively with core bacteria from the genera Nakamurella, Hyphomicrobium, Nitrospira, Paenarthrobacter, Rhodococcus, and Stenotrophobacter. The quantitative polymerase chain reaction (qPCR) results demonstrated that the CS@NGS enhanced the expressions of amoA, nxrB, nirK, nirS, narG, and napA nitrogen metabolism-related functional genes to varying degrees. The present study findings can assist the rapid start-up of aquaculture biofilters utilised to solve high nitrite and ammonia accumulation in recirculated water from industrial RAS.

A full-view management method based on artificial neural networks for energy and material-savings in wastewater treatment plants

Carbon neutrality has been received extensive attention in the field of wastewater treatment. The optimal management of wastewater treatment plants (WWTPs) has great significance and urgency since the serious energy and materials waste. In this study, a full-view management method based on artificial neural networks (ANNs) for energy and material savings in WWTPs was established. More than 5 years of historical operating data from two typical plants (size 40,000 t/d and 10,000 t/d) located in Chongqing, China, were obtained, and public data in the service area of each plant were systematically collected from open channels. These abundant historical and public data were used to train two ANNs (GRA-CNN-LSTM model and PCA-BPNN model) to predict the inlets/outlets wastewater quality and quantity. The overall average prediction accuracy of inlets/outlets wastewater indicators are greater than 92.60% and 93.76%, respectively. By combining the two models, more appropriate process operation strategies can be obtained 2 weeks in advance, with more than 11.20% and 16.91% reduction of energy and material costs, respectively. This proposed method can provide full-view decision support for the optimal management of WWTPs and is also expected to support carbon emission control and carbon neutrality in the field of wastewater treatment.

Machine learning exhibited excellent advantages in the performance simulation and prediction of free water surface constructed wetlands

Optimizing the design and operation parameters of free water surface constructed wetlands (FWS CWs) in runoff regulation and wastewater treatment is necessary to improve the comprehensive performance. In this study, nine machine learning (ML) algorithms were successfully developed to optimize the parameter combinations for FWS CWs. The scale effect of surface area on wetland performance was determined based on consistently smaller predictions (-6.2% to -28.9%) of the nine well-established ML algorithms. The models most suitable for FWS CW performance simulation and prediction were random forest and extra trees algorithms because of their high R² values (0.818 in both) with the training set and low mean absolute relative errors (4.7% and 3.8%, respectively) with the test set. Results from feature analysis of the six tree-based algorithms emphasized the importance of water depth and layout of inlet and outlet, and revealed the negligible effect of the aspect ratio. Feature importance and partial dependence analysis enhanced the interpretability of the tree-based algorithms. The proposed ML algorithms enabled the implementation of an extended scenario at a low cost in real time. Therefore, ML algorithms are suitable for expressing the complex and uncertain effects of the design and operation parameters on the performance of FWS CWs. Acquiring datasets consisting of more extensive, uniform, and unbiased parameter combinations is crucial for developing more robust and practical ML algorithms for the optimal design of FWS CWs.

Unveiling organic loading shock-resistant mechanism in a pilot-scale moving bed biofilm reactor-assisted dual-anaerobic-anoxic/oxic system for effective municipal wastewater treatment

Microbial biomass and activity are frequently subjected to organic loading shock (OLS) from decentralized municipal wastewater. A hybrid moving bed biofilm reactor-assisted dual-anaerobic-anoxic/oxic system (D-A²MBBR) was established by integrating dual-anaerobic-anoxic/oxic with moving bed biofilm reactor to resist OLS for stable nutrients removal. The D-A²MBBR achieved 91.57% of chemical oxygen demand, 93.33% of ammonia-nitrogen, 80.20% of total nitrogen and 92.68% of total phosphorus removal, respectively, under the fluctuation of organic loading rate from 417.9 to 812.0 g COD m^-3 d^-1. The 16S rRNA gene sequencing revealed that Gemmobacter (7.28%) was identified as dominating anoxic denitrifying genus in oxic chamber, confirming the coexistence of aerobic and anaerobic/anoxic micro-environments. This circumstance boosted simultaneous nitrification-denitrification and phosphorus removal and the microbial community evolution inside the multilayer biocarrier-attached biofilms. In general, the D-A²MBBR was able to provide unique, cooperative and robust bacterial consortia to form a buffer against OLS, and ensuring effluent stability.

Utilizing artificial neural network to simulate and predict the hydraulic performance of free water surface constructed wetlands

Optimizing the hydraulic performance of free water surface constructed wetlands (FWS CWs) is of great economic and ecological value. However, there is a complex nonlinear relationship between the hydraulic performance and design parameters of FWS CWs. In this study, an artificial neural network (ANN) was applied to simulate and predict the hydraulic performance corresponding to different combinations of design parameters, and orthogonal design L₉ (3⁴) was used to determine the optimal combination of the important hyperparameters of the ANN. Based on the convenient scenario prediction ability of ANN, sensitivity analysis of different design parameters was carried out by the control variate method and full factor experiment. The results showed that the combination of 3 hidden layers, 15 neural nodes in each hidden layer, 0.001 learning rate, and 8 batch sizes was optimal for the established ANN model, achieving a coefficient of determination of 0.828 in the validation set and a satisfactory prediction effect in the test set. The narrow feature distribution interval in the training set restricted the generalization ability of the ANN model to some extent. Of the four continuous design parameters, the water depth and aspect ratio had an important influence on the effective volume ratio. The layout of inlet and outlet was the most influential design parameter, as confirmed by the full factor experiment of five factors and four levels. The established ANN allowed real-time implementation in an extended scenario at a low cost. This study suggests that the ANN can simultaneously project complex and uncertain effects of several design parameters on wetland performance. In future research, acquiring further comprehensive, impartial, and unbiased experimental datasets is necessary to establish a more robust and generalizing ANN model that can guide the optimal design of FWS CWs.

Comparison of decabromodiphenyl ether degradation in long-term operated anaerobic bioreactors under thermophilic and mesophilic conditions and the pathways involved

Anaerobic digestion of decabromodiphenyl ether was carried out and compared in two continuously stirred anaerobic bioreactors for 210 days under thermophilic and mesophilic conditions. Results show that the degradation of decabromodiphenyl ether followed the first-order reaction kinetics, which exhibited a higher removal rate in the thermophilic reactor when compared to the mesophilic one, reaching its maximum of 1.1 μg·day^-1. The anaerobic digestion of decabromodiphenyl ether was found to involve the replacement of bromines from polybrominated diphenyl ether by hydrogen atoms, gradually forming nona-, octa- and hepta-brominated diphenyl ether, respectively. Under the thermophilic condition, the reactors were dominated by Bacillus sp. and Methanosarcina sp. with high bioactivity and high concentrations of debromination microorganisms.

Forecast of the outbreak of COVID-19 using artificial neural network: Case study Qatar, Spain, and Italy

The present study illustrates the outbreak prediction and analysis on the growth and expansion of the COVID-19 pandemic using artificial neural network (ANN). The first wave of the pandemic outbreak of the novel Coronavirus (SARS-CoV-2) began in September 2019 and continued to March 2020. As declared by the World Health Organization (WHO), this virus affected populations all over the globe, and its accelerated spread is a universal concern. An ANN architecture was developed to predict the serious pandemic outbreak impact in Qatar, Spain, and Italy. Official statistical data gathered from each country until July 6th was used to validate and test the prediction model. The model sensitivity was analyzed using the root mean square error (RMSE), the mean absolute percentage error and the regression coefficient index R2, which yielded highly accurate values of the predicted correlation for the infected and dead cases of 0.99 for the dates considered. The verified and validated growth model of COVID-19 for these countries showed the effects of the measures taken by the government and medical sectors to alleviate the pandemic effect and the effort to decrease the spread of the virus in order to reduce the death rate. The differences in the spread rate were related to different exogenous factors (such as social, political, and health factors, among others) that are difficult to measure. The simple and well-structured ANN model can be adapted to different propagation dynamics and could be useful for health managers and decision-makers to better control and prevent the occurrence of a pandemic.

Evolutionary Overview and Prediction of Themes in the Field of Land Degradation

Land degradation has become one of the major global environmental problems threatening human well-being. Whether degraded land can be restored has a profound effect on the achievement of the 2030 UN Sustainable Development Goals. Therefore, the ways by which to identify the current research status and potential research topics in the massive scientific literature data in the field of land degradation is a crucial issue for scientific research institutions in various countries. In view of the shortcomings in the current research on the thematic evolution and thematic and thematic prediction, such as the ignorance of random features during scientific innovation, the defects of manual classification, and the difficulty of identifying technical terms, this research proposes a new combined method. First, the Latent Dirichlet Allocation (LDA) algorithm in machine learning is used to capture the potential clustering of themes in the literature sample set of land degradation research. The distribution characteristics and evolution of themes in each period are then analyzed. The method is combined with the Hidden Markov Model (HMM), which contains double stochastic process to quantitatively predict the trend of future thematic evolution. Finally, the above-mentioned combined method is used to analyze the evolution characteristics and future development trends of the themes in the field of land degradation. Comparative experiments show that the method in this study is effective and practical. The research results show that rangeland degradation, surface temperature, island, soil degradation, water quality, crop productivity and restoration are important research topics in the field of land degradation in the future. In addition, based on the advantages of this model, this model can be widely used in the thematic evolution and prediction analysis of different research fields in land use science.