The present disclosure is directed to a treatment system in a lagoon containing water that promotes the formation of biologically active granules that digest sludge in the lagoon, the lagoon comprising a bottom thereof, the water of the lagoon having a surface layer, the system including X number of water circulators in a cluster having an impeller disposed in the lagoon, wherein X is greater than or equal to three and hydraulic walls formed from at least some of the water expelled from each of a given pair of adjacent water circulators, wherein each of the hydraulic walls intersects at the midpoint of any two adjacent circulators, said hydraulic wall redirecting the expelled water downward towards the bottom of the lagoon, wherein the hydraulic walls at least partially surround at least one circulator.

A method for building a predictive model of mDON in wastewater, including a) acquiring a kinetics associated with production and consumption of a mDON of an activated sludge system, and importing a kinetic expression of the mDON into a conventional activated sludge model No. 1 (ASM1) to build a kinetic equation for the mDON; b) inputting component variables, parameter variables, model matrices, process rate equation and operating parameters of a predictive model into a simulation software AquaSim to build an ASM-mDON model; c) inputting initial values of the component variables and the parameter variables into the simulation software AquaSim for model initialization; d) acquiring initial mDON kinetic and sensitivity analysis results, selecting corresponding parameters, calibrating kinetic and stoichiometric parameters of the ASM-mDON model using a parameter estimation function of the simulation software AquaSim; and e) replacing the initial values of the ASM-mDON model with optimal values obtained in d).

An intelligent identification method of sludge bulking based on type-2 fuzzy-neural-network belongs to the field of intelligent detection technology. The sludge volume index (SVI) in wastewater treatment plant is an important index to measure the sludge bulking of activated sludge process. However, poor production conditions and serious random interference in sewage treatment process are characterized by strong coupling, large time-varying and serious hysteresis, which makes the detection of SVI concentration of sludge volume index extremely difficult. At the same time, there are many types of sludge bulking faults, which are difficult to identify effectively. Due to the sludge volume index (SVI) is unable to online monitoring and the fault type of sludge bulking is difficult to determined, the invention develop soft-computing model based on type-2 fuzzy-neural-network to complete the real-time detection of sludge volume index (SVI). Combined with the target-related identification algorithm, the fault type of sludge bulking is determined. Results show that the intelligent identification method can quickly obtain the sludge volume index (SVI), accurate identification fault type of sludge bulking, improve the quality and ensure the safety operation of the wastewater treatment process.

A calculator calculates, using a model of a process relating to water treatment, an output variable including an effluent water quality indicating a quality of effluent water flowing out of the process based on input variables including an influent water quality indicating a quality of influent water flowing into the process and a manipulated value for the process. The calculator acquires a combination satisfying a predetermined constraint condition among combinations of the manipulated value and the output variable. A controller controls the process based on the manipulated value in the combination acquired by the calculator. A calibrator regenerates a parameter representing a characteristic of the model at regular intervals, and replaces the parameter of the model with the regenerated parameter when the effluent water quality calculated according to the regenerated parameter is closer to a measured value of the effluent water quality than the effluent water quality calculated according to the parameter before regeneration.

The disclosed lagoon biological treatment system helps existing wastewater treatment facilities meet stricter discharge permits mandated by the EPA utilizing a facility's existing wastewater treatment infrastructure. Influent is pumped into and processed in an aerated or non-aerated lagoon system, thus initially treating the wastewater to reduce BOD5 (Biochemical Oxygen Demand) and TSS (Total Suspended Solids) to approximately 20-30 mg/L. Then the wastewater is transferred to and processed in a nitrification reactor, where sufficient nitrifying bacteria is present to reduce nitrogen levels to regulation-acceptable levels without needing to regulate temperature of the water in the nitrification reactor. Wastewater may also be further processed in a denitrifying reactor if necessary to meet local requirement. Post-nitrification polishing of the wastewater is foregone.

The present invention discloses a filler component based on the Internet of Things (IoT). The filler component includes a main board, a first piece, a second piece, an accessory piece, a plurality of first through holes, and a plurality of second through holes. The main board includes a first curved surface and a second curved surface arranged opposite to each other and that are configured to form a double elliptical cross structure having a cavity. The first piece, the second piece, and the accessory piece are respectively fixed in the cavity of the main board. The first and second pieces are perpendicular to each other, and the accessory piece is parallel to the second piece and perpendicular to the first piece. The plurality of first through holes is arranged on the main board; the plurality of second through holes is arranged on the first piece and/or second piece.

A dynamic multi-objective particle swarm optimization based optimal control method is provided to realize the control of dissolved oxygen (S.sub.O) and the nitrate nitrogen (S.sub.NO) in wastewater treatment process. In this method, dynamic multi-objective particle swarm optimization was used to optimize the operation objectives of WWTP, and the optimal solutions of S.sub.O and S.sub.NO can be calculated. Then PID controller was introduced to trace the dynamic optimal solutions of S.sub.O and S.sub.NO. The results demonstrated that the proposed optimal control strategy can address the dynamic optimal control problem, and guarantee the efficient and stable operation. In addition, this proposed optimal control method in this present invention can guarantee the effluent qualities and reduce the energy consumption.

An advanced nitrogen removal method using partial nitrification-denitrification coupled two-stage autotrophic denitrification. Sewage is introduced into a first pool for partial nitrification-denitrification treatment, and then introduced into a first regulating reservoir. Dissolved oxygen content in the first pool is kept at 0.4-0.6 mg/L. Water is discharged when a molar ratio of nitrite nitrogen to ammonia nitrogen in the first regulating reservoir is 1.0-1.3:1. Effluent in the regulating reservoir is introduced into a second pool for anaerobic ammonia oxidation treatment, and then introduced into a second regulating reservoir. In the second pool, pH is 7.0-7.4, a temperature is 22-28 C. Effluent in the second regulating reservoir and sulfides are introduced into a third pool for denitrification treatment. Water is discharged. In the third pool, pH is 7.5-8.0, a temperature is 28-32 C., a mass ratio of sulfur to nitrogen is 1.9-2.0:1.

Methods and systems for electrochemical treatment of fertigation water for use and for recycling in agricultural systems such as in controlled environment agricultural systems.

An organic wastewater treatment device includes a biological treatment tank in which biological treatment units are connected in series along a flow of organic wastewater. Each biological treatment unit has a pair of an anoxic tank disposed on an upstream side, and an aerobic tank disposed on a downstream side in which a membrane separation device is immersed in activated sludge. The activated sludge returns from a most downstream-side aerobic tank to a most upstream-side anoxic tank through a sludge return path. Whether to stop an operating membrane separation device and whether to start a stopped membrane separation device are determined for each biological treatment unit based on at least one of an inflow amount of the organic wastewater, a tank water level, a transmembrane pressure difference of each membrane separation device, a T-N concentration of the treated water, and an NH3-N concentration of the treated water as an index.