[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Zaghloul et al., 2021 - Google Patents

Development of an ensemble of machine learning algorithms to model aerobic granular sludge reactors

Zaghloul et al., 2021

Document ID
6737503575886193556
Author
Zaghloul M
Iorhemen O
Hamza R
Tay J
Achari G
Publication year
Publication venue
Water research

External Links

Snippet

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 …
Continue reading at www.sciencedirect.com (other versions)

Classifications

    • C—CHEMISTRY; METALLURGY
    • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F3/00—Biological treatment of water, waste water, or sewage
    • C02F3/02—Aerobic processes
    • C02F3/12—Activated sludge processes
    • C02F3/1205—Particular type of activated sludge processes
    • C02F3/1231—Treatments of toxic sewage
    • C—CHEMISTRY; METALLURGY
    • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F3/00—Biological treatment of water, waste water, or sewage
    • C02F3/006—Regulation methods for biological treatment
    • C—CHEMISTRY; METALLURGY
    • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F3/00—Biological treatment of water, waste water, or sewage
    • C02F3/30—Aerobic and anaerobic processes
    • C02F3/302—Nitrification and denitrification treatment
    • C02F3/303—Nitrification and denitrification treatment characterised by the nitrification
    • C—CHEMISTRY; METALLURGY
    • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F2209/00—Controlling or monitoring parameters in water treatment
    • C02F2209/22—O2
    • C—CHEMISTRY; METALLURGY
    • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F3/00—Biological treatment of water, waste water, or sewage
    • C02F3/30—Aerobic and anaerobic processes
    • C02F3/308—Biological phosphorus removal
    • C—CHEMISTRY; METALLURGY
    • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F3/00—Biological treatment of water, waste water, or sewage
    • C02F3/30—Aerobic and anaerobic processes
    • C02F3/301—Aerobic and anaerobic treatment in the same reactor
    • C—CHEMISTRY; METALLURGY
    • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F2209/00—Controlling or monitoring parameters in water treatment
    • C02F2209/14—NH3-N
    • C—CHEMISTRY; METALLURGY
    • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F3/00—Biological treatment of water, waste water, or sewage
    • C02F3/32—Biological treatment of water, waste water, or sewage characterised by the animals or plants used, e.g. algae

Similar Documents

Publication Publication Date Title
Zaghloul et al. Development of an ensemble of machine learning algorithms to model aerobic granular sludge reactors
Van Loosdrecht et al. Twenty-five years of ASM1: past, present and future of wastewater treatment modelling
Cakmakci Adaptive neuro-fuzzy modelling of anaerobic digestion of primary sedimentation sludge
Zaghloul et al. Performance prediction of an aerobic granular SBR using modular multilayer artificial neural networks
Almomani Prediction the performance of multistage moving bed biological process using artificial neural network (ANN)
Xie et al. Simulation and optimization of a full-scale Carrousel oxidation ditch plant for municipal wastewater treatment
Kundu et al. Artificial neural network modeling for biological removal of organic carbon and nitrogen from slaughterhouse wastewater in a sequencing batch reactor
Khatri et al. Artificial neural network based models for predicting the effluent quality of a combined upflow anaerobic sludge blanket and facultative pond: Performance evaluation and comparison of different algorithms
CN111762958A (en) Deep well aeration process optimization method and device for sewage treatment plant based on ASM2D model
Zaghloul et al. A review of mechanistic and data-driven models of aerobic granular sludge
Isanta et al. A novel control strategy for enhancing biological N-removal in a granular sequencing batch reactor: A model-based study
Iqbal et al. Optimization of an operating domestic wastewater treatment plant using elitist non-dominated sorting genetic algorithm
Amin et al. Expanding the activated sludge model no. 1 to describe filamentous bulking: The filamentous model
Fang et al. An integrated dynamic model for simulating a full-scale municipal wastewater treatment plant under fluctuating conditions
Nasr et al. Sustainable management of wastewater treatment plants using artificial intelligence techniques
Flores et al. Dissolved oxygen regulation by logarithmic/antilogarithmic control to improve a wastewater treatment process
CN117113825A (en) A calculation method for optimal kinetic parameters of activated sludge mathematical model
Van Loosdrecht et al. Modelling activated sludge processes
Sheik et al. Fuzzy Logic Control of Active Sludge–Based Wastewater Treatment Plants
Dai et al. Efficient model calibration method based on phase experiments for anaerobic–anoxic/nitrifying (A2N) two-sludge process
Choubert et al. Anoxic and aerobic values for the yield coefficient of the heterotrophic biomass: Determination at full-scale plants and consequences on simulations
Demey et al. Validation and implementation of model based control strategies at an industrial wastewater treatment plant
Zhang et al. Environmental biological model based on optimization of activated sludge process
Haimi et al. Advanced operation and control methods of municipal wastewater treatment processes in Finland
Masliev et al. On reconciliation of traditional water quality models and activated sludge models