A method of measuring concentration of dissolved organic nitrogen in sewage. The method includes: filtering a sewage sample using a filter membrane; measuring the concentrations of total dissolved nitrogen (TDN), ammonia nitrogen (NH.sub.4.sup.+), and nitric nitrogen (NO.sub.3.sup.) in the sewage sample, respectively designated as C.sub.TDN(I), C.sub.NH4.sup.+.sub.(I) and C.sub.NO3.sup..sub.(I); calculating the ratios of (C.sub.NH4.sup.+.sub.(I)+C.sub.NO3.sup..sub.(I))/C.sub.TDN(I) and C.sub.NO3.sup..sub.(I)/C.sub.NH4.sup.+.sub.(I), and according to the ratios, calculating the concentration of dissolved organic nitrogen (DON) in the sewage sample.

The present invention provides a method of treating wastewater in a wastewater system. The wastewater system comprises a treatment plant comprising a treatment space and a sewer system comprising a sewer space. The treatment plant further comprises a treatment inlet for supplying wastewater to the treatment system from the sewer system. The method comprises the step of: providing a treatment parameter being significant for purification of wastewater in the treatment plant, determining an actual spare plant capacity indicating an amount of wastewater which can be supplied to the treatment space, and determining an actual spare wastewater storage volume indicating an amount of wastewater which can be retained in the sewer space. The amount of wastewater supplied through the treatment inlet to the treatment plant is varied based on the treatment parameter, the actual spare plant capacity, and the actual spare wastewater storage volume.

Described herein are systems and methods of analyzing data acquired from a water plant, both historical and in real-time, making determinations about process and asset health diagnosis and anomaly detection using advanced techniques, and controlling the plant and/or providing alerts based on such determinations.

A method for achieving rapid startup of a denitrification biofilter tank, which belongs to the technical field of biofilm sewage treatment. The specific steps are: 1. selecting heterotrophic denitrification or mixotrophic denitrification to treat influent sewage; 2. when the heterotrophic denitrification is used, pretreating the filter material with sodium carboxymethyl cellulose solution and then adding rhamnolipid after the introduction of sewage until the biofilter tank system starts successfully; 3. when the mixotrophic denitrification method is used, the filter tank is inoculated after the introduction of sewage, and the rhamnolipid is added thereto and is changed to sulfur source after operation for a while until startup is complete. The invention solves the problem that the denitrification biofilter tank in the sewage treatment is particularly slow in the mixotrophic state, and has a good application prospect.

The present disclosure discloses a method for determining optimal preservation temperature of biofilm in wastewater treatment, and belongs to the technical field of environmental engineering. The method for determining the optimum preservation temperature of the wastewater treatment biofilm constructed by the present disclosure comprises measuring the cell activity state of the biofilm by flow cytometry, and taking the preservation temperature closest to the cell activity state before preservation as the optimum preservation temperature. The method of the present disclosure can determine the optimum preservation temperature within a few hours and performs correlation analysis on the characteristic indexes of the biofilm activity recovery process to verify the reliability of the data. By using the method of the present disclosure, the step of recovering the activity of the biofilm process can be omitted, the pollutants can be discharged under the standard, and at the same time, the starting time of engineering application of the biofilm process can be effectively shortened, the long-term stable operation of the biofilm process is maintained, and the method has high industrial feasibility.

A water treatment plant controlling method including: determining whether or not there is a correlation, in water to be treated, between a water quality index and a concentration of a pollution component having no causal relationship with the water quality index, from a result of sample analysis of the water to be treated performed periodically; and (a) under a condition that there is a correlation, statistically analyzing a distribution of measurement values of the water quality index in a previous certain period of the water to be treated, and based on a result of the statistical analysis and the correlation, estimating the concentration of the pollution component of the water to be treated; and determining an operating condition of a water treatment plant for treating the water to be treated, based on the estimated concentration of the pollution component of the water to be treated; or (b) on a condition that a correlation is absent, statistically analyzing a distribution of concentration of the pollution component in all past sample analyses of the water to be treated, and based on a result of the statistical analysis, estimating the concentration of the pollution component of the water to be treated.

A process for controlling the aeration rate during the aerobic phase of a wastewater treatment process is disclosed, which comprises: (a) measuring at moment t.sub.1 the ammonium concentration [NH.sub.4.sup.+].sub.1 and nitrogen oxide concentration [NO.sub.x].sub.1 in a mixture of wastewater and microbial sludge; (b) determining a nitrogen oxide target concentration [NO.sub.x].sup.F.sub.1 at the end of the aerobic phase based on at least the current ammonium concentration [NH.sub.4.sup.+].sub.1 and the current NO.sub.x concentration [NO.sub.x].sub.1; (c) determining a setpoint [NO.sub.x].sup.SP.sub.1 based on interpolation between [NO.sub.x].sub.1 and [NO.sub.x].sup.F.sub.1; (d) adjusting the aeration rate to minimise error between [NO.sub.x].sub.1 and [NO.sub.x].sup.SP.sub.1; and (e) repeating steps (a) to (d) at further moments t.sub.1. The invention further concerns a process for the treatment of wastewater, using the process.

In this present disclosure, a computing implemented method is designed for predicting the effluent total nitrogen concentration (TN) in an urban wastewater treatment process (WWTP). The technology of this present disclosure is part of advanced manufacturing technology and belongs to both the field of control engineer and environment engineer. To improve the predicting efficiency, a recurrent self-organizing radial basis function (RBF) neural network (RSORBFNN) can adjust the structure and parameters simultaneously. This RSORBFNN is developed to implement this method, and then the proposed RSORBFNN-based method can predict the effluent TN concentration with acceptable accuracy. Moreover, online information of effluent TN concentration may be predicted by this computing implemented method to enhance the quality monitoring level to alleviate the current situation of wastewater and to strengthen the management of WWTP.

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.

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.