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Behera SK, Karthika S, Mahanty B, Meher SK, Zafar M, Baskaran D, Rajamanickam R, Das R, Pakshirajan K, Bilyaminu AM, Rene ER. Application of artificial intelligence tools in wastewater and waste gas treatment systems: Recent advances and prospects. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 370:122386. [PMID: 39260284 DOI: 10.1016/j.jenvman.2024.122386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2024] [Revised: 08/17/2024] [Accepted: 08/31/2024] [Indexed: 09/13/2024]
Abstract
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.
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Affiliation(s)
- Shishir Kumar Behera
- Process Simulation Research Group, School of Chemical Engineering, Vellore Institute of Technology, Vellore, 632 014, Tamil Nadu, India.
| | - S Karthika
- Department of Chemical Engineering, Alagappa College of Technology, Anna University, Chennai, 600 025, Tamil Nadu, India
| | - Biswanath Mahanty
- Division of Biotechnology, Karunya Institute of Technology & Sciences, Coimbatore, 641 114, Tamil Nadu, India
| | - Saroj K Meher
- Systems Science and Informatics Unit, Indian Statistical Institute, Bangalore, 560059, India
| | - Mohd Zafar
- Department of Applied Biotechnology, College of Applied Sciences & Pharmacy, University of Technology and Applied Sciences - Sur, P.O. Box: 484, Zip Code: 411, Sur, Oman
| | - Divya Baskaran
- Department of Chemical and Biomolecular Engineering, Chonnam National University, Yeosu, Jeonnam, 59626, South Korea; Department of Biomaterials, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Chennai, 600 077, Tamil Nadu, India
| | - Ravi Rajamanickam
- Department of Chemical Engineering, Annamalai University, Chidambaram, 608002, Tamil Nadu, India
| | - Raja Das
- Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Vellore, 632 014, Tamil Nadu, India
| | - Kannan Pakshirajan
- Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, Guwahati, 781 039, Assam, India
| | - Abubakar M Bilyaminu
- Department of Water Supply, Sanitation and Environmental Engineering, IHE Delft Institute for Water Education, P. O. Box 3015, 2601, DA Delft, the Netherlands
| | - Eldon R Rene
- Department of Water Supply, Sanitation and Environmental Engineering, IHE Delft Institute for Water Education, P. O. Box 3015, 2601, DA Delft, the Netherlands
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Teiri H, Dehghani M, Mohammadi F, Samaei MR, Hajizadeh Y, Pourzamani H, Rostami S. Modeling and optimization approach for phytoremediation of formaldehyde from polluted indoor air by Nephrolepis obliterata plant. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:21345-21359. [PMID: 36266594 DOI: 10.1007/s11356-022-23602-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 10/09/2022] [Indexed: 06/16/2023]
Abstract
This study aimed to model the removal of formaldehyde as an indoor air pollutant by Nephrolepis obliterata (R.Br.) J.Sm. plant using response surface methodology (RSM) and artificial neural network (ANN) models, and optimization of the models by particle swarm optimization algorithm (PSO). The data obtained in pilot-scale experiments under a controlled environment were used in this study. The effects of parameters on the removal efficiency such as formaldehyde concentration, relative humidity, light intensity, and leaf surface area were empirically investigated and considered as model parameters. The results of the RSM model, with power transformation, were in meaningful compromise with the experiments. A multilayer perceptron (MLP) neural network was also designed, and the mean of squared error (MSE), mean absolute error (MAE), and R2 were used to evaluate the network. Several training algorithms were assessed and the best one, the Levenberg Marquardt (LM), was selected. The PSO algorithm proved that the highest removal efficiency of formaldehyde was obtained in the presence of light, maximum leaf surface area and relative humidity, and at the lowest inlet concentration. The empirical system breakthrough occurred at 15 mg/m3 of formaldehyde, and the maximum elimination capacity was about 0.96 mg per m2 of leaves. The findings indicated that the ANN model predicted the removal efficiency more accurately compared to the RSM model.
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Affiliation(s)
- Hakimeh Teiri
- Student Research Committee, Department of Environmental Health Engineering, School of Health, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Mansooreh Dehghani
- Department of Environmental Health Engineering, School of Health, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Farzaneh Mohammadi
- Faculty of Health and Research Institute for Primordial Prevention of Non-Communicable Disease, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Mohammad Reza Samaei
- Department of Environmental Health Engineering, School of Health, Shiraz University of Medical Sciences, Shiraz, Iran.
| | - Yaghoub Hajizadeh
- Faculty of Health and Research Institute for Primordial Prevention of Non-Communicable Disease, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Hamidreza Pourzamani
- Faculty of Health and Research Institute for Primordial Prevention of Non-Communicable Disease, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Saeid Rostami
- Department of Environmental Health Engineering, School of Health, Shiraz University of Medical Sciences, Shiraz, Iran
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Ly QV, Truong VH, Ji B, Nguyen XC, Cho KH, Ngo HH, Zhang Z. Exploring potential machine learning application based on big data for prediction of wastewater quality from different full-scale wastewater treatment plants. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 832:154930. [PMID: 35390391 DOI: 10.1016/j.scitotenv.2022.154930] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Revised: 03/26/2022] [Accepted: 03/26/2022] [Indexed: 06/14/2023]
Abstract
Water pollution generated from intensive anthropogenic activities has emerged as a critical issue concerning ecosystem balance and livelihoods worldwide. Although optimizing wastewater treatment efficiency is widely regarded as the foremost step to minimize pollutants released into the environment, this widespread application has encountered two major problems: firstly, the significant variation of influent wastewater constituents; secondly, complex treatment processes within wastewater treatment plants (WWTPs). Based on the data collected hourly using real-time sensors in three different full-scale WWTPs (24 h × 365 days × 3 WWTPs × 10 wastewater parameters), this work introduced the potential application of Machine Learning (ML) to predict wastewater quality. In this work, six different ML algorithms were examined and compared, varying from shallow to deep learning architectures including Seasonal Autoregressive Integrated Moving Average (SARIMAX), Random Forest (RF), Support Vector Machine (SVM), Gradient Tree Boosting (GTB), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Long Short-Term Memory (LSTM). These models were developed to detect total phosphorus in the outlet (Outlet-TP), which served as an output variable due to the rising concerns about the eutrophication problem. Irrespective of WWTPs, SARIMAX consistently demonstrated the best performance for regression estimation as evidenced by the lowest values of Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and the highest coefficient of determination (R2). In terms of computation efficiency, SARIMAX exhibited acceptable time computation, acknowledging the successful application of this algorithm for Outlet-TP modeling. In contrast, the complex structure of LSTM made it time-consuming and unstable coupled with noise, while other shallower architectures, i.e., RF, SVM, GTB, and ANFIS were unable to address large datasets with nonlinear and nonstationary behavior. Consequently, this study provides a reliable and accurate approach to forecast wastewater effluent quality, which is pivotal in terms of the socio-economic aspects of wastewater management.
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Affiliation(s)
- Quang Viet Ly
- Institute of Environmental Engineering & Nano-Technology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, Guangdong, China; Guangdong Provincial Engineering Research Center for Urban Water Recycling and Environmental Safety, Tsinghua-Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, Guangdong, China; School of Environment, Tsinghua University, Beijing 100084, China
| | - Viet Hung Truong
- Faculty of Civil Engineering, Thuyloi University, 175 Tay Son, Dong Da, Hanoi 10000, Viet Nam
| | - Bingxuan Ji
- Institute of Environmental Engineering & Nano-Technology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, Guangdong, China; Guangdong Provincial Engineering Research Center for Urban Water Recycling and Environmental Safety, Tsinghua-Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, Guangdong, China; School of Environment, Tsinghua University, Beijing 100084, China
| | - Xuan Cuong Nguyen
- Institute of Research and Development, Duy Tan University, Danang 550000, Viet Nam
| | - Kyung Hwa Cho
- School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, 50 UNIST-gil, Eonyang-eup, Ulju-gun, Ulsan 689-798, South Korea
| | - Huu Hao Ngo
- School of Civil and Environmental Engineering, University of Technology Sydney, Sydney, NWS 2007, Australia
| | - Zhenghua Zhang
- Institute of Environmental Engineering & Nano-Technology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, Guangdong, China; Guangdong Provincial Engineering Research Center for Urban Water Recycling and Environmental Safety, Tsinghua-Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, Guangdong, China; School of Environment, Tsinghua University, Beijing 100084, China.
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Ran X, Zhou M, Wang T, Wang W, Kumari S, Wang Y. Multidisciplinary characterization of nitrogen-removal granular sludge: A review of advances and technologies. WATER RESEARCH 2022; 214:118214. [PMID: 35240472 DOI: 10.1016/j.watres.2022.118214] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 02/15/2022] [Accepted: 02/18/2022] [Indexed: 06/14/2023]
Abstract
Nitrogen-removal granular sludge (NRGS) is a promising technology in wastewater treatment, with advantages of efficient nitrogen removal, less footprint, lower sludge production and energy consumption, and is a way for wastewater treatment plants to achieve carbon-neutrality. Aerobic granular sludge (AGS) and anammox granular sludge (AnGS) are two typical NRGS technologies that have attracted extensive attention. Mounting evidence has shown strong associations between NRGS properties and the status of NRGS systems; however, a holistic view is still missing. The aim of this article is to provide an overview of NRGS with an emphasis on characterization. Specifically, the integrated nitrogen transformation pathways inside NRGS and the performance of NRGS treating various wastewaters are discussed. NRGS properties are categorized as physical-, chemical-, biological- and systematical ones, presenting current advances and corresponding characterization technologies. Finally, the future prospects for furthering the mechanistic understanding and engineering application of NRGS are proposed. Overall, the technological advancements in characterization have greatly contributed to understanding NRGS properties, which are potential factors for optimizing the performance and evaluating the working status of NRGS. This review will provide guidance in characterizing NRGS properties and boost the introduction of novel characterization technologies.
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Affiliation(s)
- Xiaochuan Ran
- State Key Laboratory of Pollution Control and Resources Reuse, Shanghai Institute of Pollution Control and Ecological Security, College of Environmental Science and Engineering, Tongji University, Siping Road, Shanghai 200092, China
| | - Mingda Zhou
- State Key Laboratory of Pollution Control and Resources Reuse, Shanghai Institute of Pollution Control and Ecological Security, College of Environmental Science and Engineering, Tongji University, Siping Road, Shanghai 200092, China
| | - Tong Wang
- State Key Laboratory of Pollution Control and Resources Reuse, Shanghai Institute of Pollution Control and Ecological Security, College of Environmental Science and Engineering, Tongji University, Siping Road, Shanghai 200092, China
| | - Weigang Wang
- State Key Laboratory of Pollution Control and Resources Reuse, Shanghai Institute of Pollution Control and Ecological Security, College of Environmental Science and Engineering, Tongji University, Siping Road, Shanghai 200092, China
| | - Sheena Kumari
- Institute for Water and Wastewater Technology, Durban University of Technology, P.O. Box 1334, Durban 4000, South Africa
| | - Yayi Wang
- State Key Laboratory of Pollution Control and Resources Reuse, Shanghai Institute of Pollution Control and Ecological Security, College of Environmental Science and Engineering, Tongji University, Siping Road, Shanghai 200092, China.
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Support Vector Regression Modelling of an Aerobic Granular Sludge in Sequential Batch Reactor. MEMBRANES 2021; 11:membranes11080554. [PMID: 34436317 PMCID: PMC8400290 DOI: 10.3390/membranes11080554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 07/08/2021] [Accepted: 07/16/2021] [Indexed: 11/18/2022]
Abstract
Support vector regression (SVR) models have been designed to predict the concentration of chemical oxygen demand in sequential batch reactors under high temperatures. The complex internal interaction between the sludge characteristics and their influent were used to develop the models. The prediction becomes harder when dealing with a limited dataset due to the limitation of the experimental works. A radial basis function algorithm with selected kernel parameters of cost and gamma was used to developed SVR models. The kernel parameters were selected by using a grid search method and were further optimized by using particle swarm optimization and genetic algorithm. The SVR models were then compared with an artificial neural network. The prediction results R2 were within >90% for all predicted concentration of COD. The results showed the potential of SVR for simulating the complex aerobic granulation process and providing an excellent tool to help predict the behaviour in aerobic granular reactors of wastewater treatment.
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Zaghloul MS, Iorhemen OT, Hamza RA, Tay JH, Achari G. Development of an ensemble of machine learning algorithms to model aerobic granular sludge reactors. WATER RESEARCH 2021; 189:116657. [PMID: 33248333 DOI: 10.1016/j.watres.2020.116657] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 10/17/2020] [Accepted: 11/18/2020] [Indexed: 06/12/2023]
Abstract
Machine learning models provide an adaptive tool to predict the performance of treatment reactors under varying operational and influent conditions. Aerobic granular sludge (AGS) is still an emerging technology and does not have a long history of full-scale application. There is, therefore, a scarcity of long-term data in this field, which impacted the development of data-driven models. In this study, a machine learning model was developed for simulating the AGS process using 475 days of data collected from three lab-based reactors. Inputs were selected based on RReliefF ranking after multicollinearity reduction. A five-stage model structure was adopted in which each parameter was predicted using separate models for the preceding parameters as inputs. An ensemble of artificial neural networks, support vector regression and adaptive neuro-fuzzy inference systems was used to improve the models' performance. The developed model was able to predict the MLSS, MLVSS, SVI5, SVI30, granule size, and effluent COD, NH4-N, and PO43- with average R2, nRMSE and sMAPE of 95.7%, 0.032 and 3.7% respectively.
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Affiliation(s)
- Mohamed Sherif Zaghloul
- Department of Civil Engineering, University of Calgary, 2500 University Drive NW, Calgary, AB., Canada. T2N 1N4.
| | | | | | - Joo Hwa Tay
- Department of Civil Engineering, University of Calgary, 2500 University Drive NW, Calgary, AB., Canada. T2N 1N4
| | - Gopal Achari
- Department of Civil Engineering, University of Calgary, 2500 University Drive NW, Calgary, AB., Canada. T2N 1N4
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7
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Performance prediction of an internal-circulation membrane bioreactor based on models comparison and data features analysis. Biochem Eng J 2021. [DOI: 10.1016/j.bej.2020.107850] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
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Meng X, Zhang Y, Qiao J. An adaptive task-oriented RBF network for key water quality parameters prediction in wastewater treatment process. Neural Comput Appl 2021. [DOI: 10.1007/s00521-020-05659-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Ansari M, Othman F, El-Shafie A. Optimized fuzzy inference system to enhance prediction accuracy for influent characteristics of a sewage treatment plant. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 722:137878. [PMID: 32199382 DOI: 10.1016/j.scitotenv.2020.137878] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2019] [Revised: 02/26/2020] [Accepted: 03/10/2020] [Indexed: 06/10/2023]
Abstract
Sewage treatment plants (STPs) keep sewage contamination within safe levels and minimize the risk of environmental disasters. To achieve optimum operation of an STP, it is necessary for influent parameters to be measured or estimated precisely. In this research, six well-known influent chemical and biological characteristics, i.e., biochemical oxygen demand (BOD), chemical oxygen demand (COD), Ammoniacal Nitrogen (NH3-N), pH, oil and grease (OG) and suspended solids (SS), were modeled and predicted using the Sugeno fuzzy logic model. The membership function range of the fuzzy model was optimized by ANFIS, the integrated Genetic algorithms (GA), and the integrated particle swarm optimization (PSO) algorithms. The results were evaluated by different indices to find the accuracy of each algorithm. To ensure prediction accuracy, outliers in the predicted data were found and replaced with reasonable values. The results showed that both integrated GA-FIS and PSO-FIS algorithms performed at almost the same level and both had fewer errors than ANFIS. As the GA-FIS algorithm predicts BOD with fewer errors than PSO-FIS and the aim of this study is to provide an accurate prediction of missing data, GA-FIS was only used to predict the BOD parameter; the other parameters were predicted by PSO-FIS algorithm. As a result, the model successfully could provide outstanding performance for predicting the BOD, COD, NH3-N, OG, pH and SS with MAE equal to 3.79, 5.14, 0.4, 0.27, 0.02, and 3.16, respectively.
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Affiliation(s)
- Mozafar Ansari
- Department of Civil Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia
| | - Faridah Othman
- Department of Civil Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia.
| | - Ahmed El-Shafie
- Department of Civil Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia
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Dahlan I, Hassan SR, Lee WJ. Modeling of modified anaerobic baffled reactor for recycled paper mill effluent treatment using response surface methodology and artificial neural network. SEP SCI TECHNOL 2020. [DOI: 10.1080/01496395.2020.1728321] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Affiliation(s)
- Irvan Dahlan
- School of Chemical Engineering, Universiti Sains Malaysia, Nibong Tebal, Malaysia
- Solid Waste Management Cluster, Science and Engineering Research Centre, Universiti Sains Malaysia, Nibong Tebal, Malaysia
| | - Siti Roshayu Hassan
- Faculty of Bioengineering and Technology, Universiti Malaysia Kelantan, Jeli, Malaysia
| | - Wen Jie Lee
- School of Chemical Engineering, Universiti Sains Malaysia, Nibong Tebal, Malaysia
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Mohammadi F, Samaei MR, Azhdarpoor A, Teiri H, Badeenezhad A, Rostami S. Modelling and Optimizing Pyrene Removal from the Soil by Phytoremediation using Response Surface Methodology, Artificial Neural Networks, and Genetic Algorithm. CHEMOSPHERE 2019; 237:124486. [PMID: 31398609 DOI: 10.1016/j.chemosphere.2019.124486] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Revised: 07/11/2019] [Accepted: 07/29/2019] [Indexed: 05/26/2023]
Abstract
This study aimed to model and optimize pyrene removal from the soil contaminated by sorghum bicolor plant using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) with Genetic Algorithm (GA) approach. Here, the effects of indole acetic acid (IAA) and pseudomonas aeruginosa bacteria on increasing pyrene removal efficiency by phytoremediation process was studied. The experimental design was done using the Box-Behnken Design (BBD) technique. In the RSM model, the non-linear second-order model was in good agreement with the laboratory results. A two-layer Feed-Forward Back-Propagation Neural Network (FFBPNN) model was designed. Various training algorithms were evaluated and the Levenberg Marquardt (LM) algorithm was selected as the best one. Existence of eight neurons in the hidden layer leads to the highest R and lowest MSE and MAE. The results of the GA determined the optimum performance conditions. The results showed that using indole acetic acid and pseudomonas bacteria increased the efficiency of the sorghum plant in removing pyrene from the soil. The comparison obviously indicated that the prediction capability of the ANN model was much better than that of the RSM model.
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Affiliation(s)
- Farzaneh Mohammadi
- Department of Environmental Health Engineering, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Mohammad Reza Samaei
- Department of Environmental Health Engineering, School of Health, Shiraz University of Medical Sciences, Shiraz, Iran.
| | - Abooalfazl Azhdarpoor
- Department of Environmental Health Engineering, School of Health, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Hakimeh Teiri
- Department of Environmental Health Engineering, School of Health, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Ahmad Badeenezhad
- Department of Environmental Health Engineering, School of Health, Behbahan Faculty of Medical Sciences, Behbahan, Iran
| | - Saeid Rostami
- Department of Environmental Health Engineering, School of Health, Shiraz University of Medical Sciences, Shiraz, Iran.
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Antwi P, Zhang D, Xiao L, Kabutey FT, Quashie FK, Luo W, Meng J, Li J. Modeling the performance of Single-stage Nitrogen removal using Anammox and Partial nitritation (SNAP) process with backpropagation neural network and response surface methodology. THE SCIENCE OF THE TOTAL ENVIRONMENT 2019; 690:108-120. [PMID: 31284185 DOI: 10.1016/j.scitotenv.2019.06.530] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Revised: 06/29/2019] [Accepted: 06/30/2019] [Indexed: 06/09/2023]
Abstract
Two novel feedforward backpropagation Artificial Neural Networks (ANN)-based-models (8:NH:1 and 7:NH:1) combined with Box-Behnken design of experiments methodology was proposed and developed to model NH4+ and Total Nitrogen (TN) removal within an upflow-sludge-bed (USB) reactor treating nitrogen-rich wastewater via Single-stage Nitrogen removal using Anammox and Partial nitritation (SNAP) process. ANN were developed by optimizing network architecture parameters via response surface methodology. Based on the goodness-of-fit standards, the proposed three-layered NH4+ and TN removal ANN-based-models trained with Levenberg-Marquardt-algorithm demonstrated high-performance as computations exhibited smaller deviations-(±2.1%) as well as satisfactory coefficient of determination (R2), fractional variance-(FV), and index of agreement-(IA) ranging 0.989-0.997, 0.003-0.031 and 0.993-0.998, respectively. The computational results affirmed that the ANN architecture which was optimized with response surface methodology enhanced the efficiency of the ANN-based-models. Furthermore, the overall performance of the developed ANN-based models revealed that modeling intricate biological systems (such as SNAP) using ANN-based models with the view to improve removal efficiencies, establish process control strategies and optimize performance is highly feasible. Microbial community analysis conducted with 16S rRNA high-throughput approach revealed that Candidatus Kuenenia was the most pronounced genera which accounted for 13.11% followed by Nitrosomonas-(6.23%) and Proteocatella-(3.1%), an indication that nitrogen removal pathway within the USB was mainly via partial-nitritation/anammox process.
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Affiliation(s)
- Philip Antwi
- Jiangxi University of Science and Technology, School of Resources and Environmental Engineering, Jiangxi Key Laboratory of Mining & Metallurgy Environmental Pollution Control, Jiangxi Province, Ganzhou City 341000, China.
| | - Dachao Zhang
- Jiangxi University of Science and Technology, School of Resources and Environmental Engineering, Jiangxi Key Laboratory of Mining & Metallurgy Environmental Pollution Control, Jiangxi Province, Ganzhou City 341000, China.
| | - Longwen Xiao
- Jiangxi University of Science and Technology, School of Resources and Environmental Engineering, Jiangxi Key Laboratory of Mining & Metallurgy Environmental Pollution Control, Jiangxi Province, Ganzhou City 341000, China.
| | - Felix Tetteh Kabutey
- Harbin Institute of Technology, School of Environmental, State Key Laboratory of Urban Water Resource and Environment, 73 Huanghe Road, Harbin 150090, China
| | - Frank Koblah Quashie
- Jiangxi University of Science and Technology, School of Resources and Environmental Engineering, Jiangxi Key Laboratory of Mining & Metallurgy Environmental Pollution Control, Jiangxi Province, Ganzhou City 341000, China
| | - Wuhui Luo
- Jiangxi University of Science and Technology, School of Resources and Environmental Engineering, Jiangxi Key Laboratory of Mining & Metallurgy Environmental Pollution Control, Jiangxi Province, Ganzhou City 341000, China
| | - Jia Meng
- Harbin Institute of Technology, School of Environmental, State Key Laboratory of Urban Water Resource and Environment, 73 Huanghe Road, Harbin 150090, China; University of Queensland, Advanced Water Management Centre, Gehrman Building, Research Road, The St Lucia, Brisbane, QLD 4072, Australia
| | - Jianzheng Li
- Harbin Institute of Technology, School of Environmental, State Key Laboratory of Urban Water Resource and Environment, 73 Huanghe Road, Harbin 150090, China
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Artificial Neural Network (ANN) Approach to Modelling of Selected Nitrogen Forms Removal from Oily Wastewater in Anaerobic and Aerobic GSBR Process Phases. WATER 2019. [DOI: 10.3390/w11081594] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Paper presents artificial neural network models (ANN) approximating concentration of selected nitrogen forms in wastewater after sequence batch reactor operating with aerobic granular activated sludge (GSBR) in the anaerobic and aerobic phases. Aim of the study was to determine parameters conditioning effectiveness of selected nitrogen forms removal in GSBR reactor process phases. Models of artificial neural networks were developed separately for N-NH4, N-NO3 and total nitrogen concentration in particular process phases of GSBR reactor. In total, 6 ANN models were presented in this paper. ANN models were made as multilayer perceptron (MLP), which were learned using the Broyden-Fletcher-Goldfarb-Shanno algorithm. Developed ANN models indicated variables the most influencing of particular nitrogen forms in aerobic and anaerobic phase of GSBR reactor. Concentration of estimated nitrogen form at the beginning of anaerobic or aerobic phase, depending on ANN model, in all ANN models influenced approximated value. Obtained determination coefficients varied from 0.996 to 0.999 and were depending on estimated nitrogen form and GSBR process phase. Hence, developed ANN models can be used in further studies on modeling of nitrogen forms in anaerobic and aerobic phase of GSBR reactors.
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