Bio-communal wastewater treatment plant real-time modeling using an intelligent meta-heuristic approach: A sustainable and green ecosystem

[1]  Chongchong Qi,et al.  Machine learning exploration of the mobility and environmental assessment of toxic elements in mining-associated solid wastes , 2023, Journal of Cleaner Production.

[2]  M. M. Al Khalidy,et al.  Development of local and global wastewater biochemical oxygen demand real-time prediction models using supervised machine learning algorithms , 2023, Eng. Appl. Artif. Intell..

[3]  Xiaodan Zhao,et al.  Nitrogen recovery from wastewater as nitrate by coupling mainstream ammonium separation with side stream cyclic up-concentration and targeted conversion , 2022, Chemical Engineering Journal.

[4]  S. Abba,et al.  Prediction of energy content of biomass based on hybrid machine learning ensemble algorithm , 2022, Energy Nexus.

[5]  Lehua Zhang,et al.  Intelligent control of the electrochemical nitrate removal basing on artificial neural network (ANN) , 2022, Journal of Water Process Engineering.

[6]  V. Mishra,et al.  Analysing the effects of culture parameters on wastewater treatment capability of microalgae through association rule mining , 2022, Journal of Environmental Chemical Engineering.

[7]  Tao Chen,et al.  Prediction of uranium adsorption capacity on biochar by machine learning methods , 2022, Journal of Environmental Chemical Engineering.

[8]  I. Farooqi,et al.  A comprehensive review of Design of experiment (DOE) for water and wastewater treatment application - Key concepts, methodology and contextualized application , 2022, Journal of Water Process Engineering.

[9]  F. Ashour,et al.  Artificial Neural Network Modeling of Biochar Enhanced Anaerobic Sewage Sludge Digestion , 2022, Journal of Environmental Chemical Engineering.

[10]  Lijian Leng,et al.  Machine learning predicting wastewater properties of the aqueous phase derived from hydrothermal treatment of biomass. , 2022, Bioresource technology.

[11]  Quang Viet Ly,et al.  Exploring potential machine learning application based on big data for prediction of wastewater quality from different full-scale wastewater treatment plants. , 2022, The Science of the total environment.

[12]  Z. Iqbal,et al.  Recent advances in adsorptive removal of wastewater pollutants by chemically modified metal oxides: A review , 2022, Journal of Water Process Engineering.

[13]  G. Achari,et al.  Application of machine learning techniques to model a full-scale wastewater treatment plant with biological nutrient removal , 2022, Journal of Environmental Chemical Engineering.

[14]  Maha M. Althobaiti,et al.  An Intelligent Carbon-Based Prediction of Wastewater Treatment Plants Using Machine Learning Algorithms , 2022, Adsorption Science & Technology.

[15]  W. El-Dakhakhni,et al.  Machine Learning Classification Algorithms for Inadequate Wastewater Treatment Risk Mitigation , 2022, Process Safety and Environmental Protection.

[16]  A. Kwade,et al.  Multi-Modal Framework to Model Wet Milling Through Numerical Simulations and Artificial Intelligence (Part 2) , 2022, SSRN Electronic Journal.

[17]  Dionysios D. Dionysiou,et al.  Applications of computational chemistry, artificial intelligence, and machine learning in aquatic chemistry research , 2021 .

[18]  M. El-Rawy,et al.  Forecasting effluent and performance of wastewater treatment plant using different machine learning techniques , 2021, Journal of Water Process Engineering.

[19]  Z. Yaseen,et al.  A new insight for real-time wastewater quality prediction using hybridized kernel-based extreme learning machines with advanced optimization algorithms , 2021, Environmental Science and Pollution Research.

[20]  Rabin Bhattarai,et al.  Prediction of Nitrate and Phosphorus Concentrations Using Machine Learning Algorithms in Watersheds with Different Landuse , 2021, Water.

[21]  Behrouz Mehdinejadiani,et al.  A comparative study on using metaheuristic algorithms for simultaneously estimating parameters of space fractional advection-dispersion equation , 2021 .

[22]  Seungdae Oh,et al.  Machine-learning insights into nitrate-reducing communities in a full-scale municipal wastewater treatment plant. , 2021, Journal of environmental management.

[23]  Thi Tuyet Hanh Nguyen,et al.  Nitrogen removal in subsurface constructed wetland: Assessment of the influence and prediction by data mining and machine learning , 2021, Environmental Technology & Innovation.

[24]  Javad Roostaei,et al.  Comparative study on total nitrogen prediction in wastewater treatment plant and effect of various feature selection methods on machine learning algorithms performance , 2021 .

[25]  Z. Yaseen An insight into machine learning models era in simulating soil, water bodies and adsorption heavy metals: Review, challenges and solutions. , 2021, Chemosphere.

[26]  R. A. Abdulkadir,et al.  A new hybrid model based on relevance vector machine with flower pollination algorithm for phycocyanin pigment concentration estimation , 2021, Environmental Science and Pollution Research.

[27]  E. R. Rene,et al.  Applications of machine learning algorithms for biological wastewater treatment: Updates and perspectives , 2021, Clean Technologies and Environmental Policy.

[28]  Zhen Liu,et al.  Using LSTM Neural Network Based on Improved PSO and Attention Mechanism for Predicting the Effluent COD in a Wastewater Treatment Plant , 2021, IEEE Access.

[29]  S. Abba,et al.  Chemometrics-based models hyphenated with ensemble machine learning for retention time simulation of Isoquercitrin in Coriander sativum L. using high performance liquid chromatography. , 2020, Journal of separation science.

[30]  F. Bux,et al.  Artificial neural network and techno-economic estimation with algae-based tertiary wastewater treatment , 2020 .

[31]  Z. Yaseen,et al.  River water quality index prediction and uncertainty analysis: A comparative study of machine learning models , 2020 .

[32]  Narendra Khatri,et al.  Artificial neural network modelling of faecal coliform removal in an intermittent cycle extended aeration system-sequential batch reactor based wastewater treatment plant , 2020 .

[33]  L. Lona,et al.  Artificial neural networks towards average properties targets in styrene ARGET-ATRP , 2020 .

[34]  Zaher Mundher Yaseen,et al.  A survey on river water quality modelling using artificial intelligence models: 2000–2020 , 2020 .

[35]  Yong Xiang,et al.  A double decomposition-based modelling approach to forecast weekly solar radiation , 2020 .

[36]  Abhijit Roy,et al.  Prediction and Control of Coke Plant Wastewater Quality using Machine Learning Techniques , 2020, Coke and Chemistry.

[37]  Annamária R. Várkonyi-Kóczy,et al.  Advances in Machine Learning Modeling Reviewing Hybrid and Ensemble Methods , 2019, Lecture Notes in Networks and Systems.

[38]  Yongzhen Peng,et al.  Recent advances in controlling denitritation for achieving denitratation/anammox in mainstream wastewater treatment plants. , 2019, Bioresource technology.

[39]  Vahid Nourani,et al.  An emotional artificial neural network for prediction of vehicular traffic noise. , 2019, The Science of the total environment.

[40]  Sinan Q. Salih,et al.  Laundry wastewater treatment using a combination of sand filter, bio-char and teff straw media , 2019, Scientific Reports.

[41]  Vahid Nourani,et al.  Artificial intelligence based ensemble model for prediction of vehicular traffic noise. , 2019, Environmental research.

[42]  E. Dacewicz Waste assessment decision support systems used for domestic sewage treatment , 2019, Journal of Water Process Engineering.

[43]  Ravinesh C. Deo,et al.  Improving SPI-derived drought forecasts incorporating synoptic-scale climate indices in multi-phase multivariate empirical mode decomposition model hybridized with simulated annealing and kernel ridge regression algorithms , 2019, Journal of Hydrology.

[44]  Narendra Khatri,et al.  Prediction of effluent quality in ICEAS-sequential batch reactor using feedforward artificial neural network. , 2019, Water science and technology : a journal of the International Association on Water Pollution Research.

[45]  Mumtaz Ali,et al.  Designing a multi-stage multivariate empirical mode decomposition coupled with ant colony optimization and random forest model to forecast monthly solar radiation , 2019, Applied Energy.

[46]  V. Singh,et al.  Novel Hybrid Evolutionary Algorithms for Spatial Prediction of Floods , 2018, Scientific Reports.

[47]  Ravinesh C. Deo,et al.  Multi-stage committee based extreme learning machine model incorporating the influence of climate parameters and seasonality on drought forecasting , 2018, Comput. Electron. Agric..

[48]  Ahmad Sharafati,et al.  The Integration of Nature-Inspired Algorithms with Least Square Support Vector Regression Models: Application to Modeling River Dissolved Oxygen Concentration , 2018, Water.

[49]  Ravinesh C. Deo,et al.  An ensemble-ANFIS based uncertainty assessment model for forecasting multi-scalar standardized precipitation index , 2018, Atmospheric Research.

[50]  G. L. Bodhe,et al.  An adaptive neuro-fuzzy interface system model for traffic classification and noise prediction , 2018, Soft Comput..

[51]  Zaher Mundher Yaseen,et al.  Hybrid Adaptive Neuro-Fuzzy Models for Water Quality Index Estimation , 2018, Water Resources Management.

[52]  Zaher Mundher Yaseen,et al.  Predicting compressive strength of lightweight foamed concrete using extreme learning machine model , 2018, Adv. Eng. Softw..

[53]  B. Mehdinejadiani Estimating the solute transport parameters of the spatial fractional advection-dispersion equation using Bees Algorithm. , 2017, Journal of contaminant hydrology.

[54]  A. Thalla,et al.  Artificial intelligence models for predicting the performance of biological wastewater treatment plant in the removal of Kjeldahl Nitrogen from wastewater , 2017, Applied Water Science.

[55]  Muhammad Sani Gaya,et al.  ANFIS Modelling of Carbon and Nitrogen Removal in Domestic Wastewater Treatment Plant , 2014 .

[56]  ChanAlison,et al.  Nutrient removal (nitrogen and phosphorous) in secondary effluent from a wastewater treatment plant by microalgae , 2014 .

[57]  Han Yi,et al.  Application of Shuffled Frog Leaping Algorithm to an Uncapacitated SLLS Problem , 2012 .

[58]  Hui-Huang Hsu,et al.  Hybrid feature selection by combining filters and wrappers , 2011, Expert Syst. Appl..

[59]  Yong Qiu,et al.  Nitrogen and Phosphorous Removal in Municipal Wastewater Treatment Plants in China: A Review , 2010 .

[60]  V. Vapnik The Support Vector Method of Function Estimation , 1998 .