A water quality measuring system includes a first introduction section for introducing rearing water as a sampling target, and a first adding section which adds an acid to the rearing water introduced by the first introduction section, and a nitrous acid sensor whose measurement target is nitrous acid and which measures the measurement target concentration of the rearing water to which the acid has been added by the first adding section. The water quality measuring system includes a second adding section which adds a base to the rearing water introduced by the first introduction section, and an ammonia sensor whose measurement target is ammonia and which measures the measurement target concentration of the rearing water to which the base has been added by the second adding section.

Provided is a water quality management apparatus for an aquaculture pond, the apparatus having a storage unit for storing water quality-related measured values measured at appropriate time intervals by external sensors set in the aquaculture pond, an assessment unit for calculating a predicted future value on the basis of fluctuations in the measured values and determining the time when the predicted value will exceed a reference value that indicates deterioration of water quality, and a display unit for displaying the time when the reference value will be exceeded. Said time is the time when exchange of pond water will be necessary.

An intelligent detection system of effluent total nitrogen (TN) based on fuzzy transfer learning algorithm belongs to the field of intelligent detection technology. To detect the TN concentration, the artificial neural network can be used to model wastewater treatment process due to the nonlinear approximation ability and learning ability. However, wastewater treatment process has the characteristic of time-varying dynamics and external disturbance, artificial neural network prediction method cannot acquire sufficient data to ensure the accuracy of TN prediction, and data loss and data deficiency will make the prediction model invalid. The invention proposed an intelligent detection system of effluent total nitrogen based on fuzzy transfer learning algorithm; the proposed system contains several functional modules, including detection instrument, data acquisition, data storage and TN prediction. For the TN prediction module, the fuzzy transfer learning algorithm build the fuzzy neural network based intelligent prediction model, which the parameters are adjusted by the transfer learning method.

The present invention is an ammonia sequestering system including a system controller connected to a plurality of flow control valves, a feed stream extending through a system inlet, and a system outlet. The feed stream is a liquid contaminated with ammonia. At least one exchange column is located between the system inlet and the system outlet. The ion column includes an ion exchange material, a column inlet connected to one of the flow control valves, and a column outlet connected to another of the flow control valves. The system also includes a regenerant stream of an aqueous solution of sodium cations, as well as an ammonia brine stream made up of the regenerant stream and ammonia.

There are provided processes for treating wastewater. The processes can comprise treating a mixture comprising the wastewater and an activated sludge, in a single reactor, with an electric current having a density of less than about 55 A/m.sup.2, by means of at least one anode and at least one cathode that define therebetween an electrical zone for treating the mixture; exposing the mixture to an intermittent ON/OFF electrical exposure mode to the electric current in which an OFF period of time is about 1 to about 10 times longer than an ON period of time; and maintaining an adequate oxidation-reduction potential in the single reactor. Such processes allow for substantial removal of carbon, nitrogen and phosphorus from the wastewater in the single reactor of various forms and for obtaining another mixture comprising a treated wastewater and solids.

Process for treating effluents containing nitrogen in ammonium form, using a sequential biological reactor (1), according to which: a volume of effluents to be treated in one complete cycle is introduced into the biological reactor in one or more successive fractions by volume, each fraction by volume being treated during a subcycle; each subcycle comprises a phase of feeding with a fraction by volume, and at least a first aerated step, during which total or partial oxidation of the ammonium to nitrites takes place by injection of air or of oxygen into the effluent, a settling out and emptying step taking place after the end of the complete cycle; the weight of N—NH.sub.4 to be treated is determined from the volume of effluents in the reactor at the end of the feeding phase, and from the difference between the initial N—NH.sub.4 concentration in the reactor and a concentration desired at the end of the aerated step, a maximum aeration time TM is dedicated to the aerated step, and an initial air flow rate Qairinit, for the beginning of the aerated step, is determined by taking into account the weight of N—NH.sub.4 to be treated and the maximum aeration time TM.

A process for reducing the production of sludge by municipal or industrial wastewater purification plants, comprising a step of mesophilic or thermophilic anaerobic digestion (20), or anaerobic digestion combining these two operating modes, of a stream of sludge to be treated (1), and at least one biological solubilization anaerobic treatment step (30); the process comprises, upstream of the anaerobic digestion step, a step of dehydration (10) of the sludge to be treated, followed by a step of mixing (15) the dehydrated sludge with a recirculated fraction of sludge that is more liquid, originating from recycling of the digestion (20), and/or from the anaerobic treatment step (30), and/or centrates originating from a final dehydration (50) of the treated sludge, wherein the recirculation rate is chosen such that the mixture has a dryness suitable for digestion, this mixture then being directed towards the digestion.

The present disclosure relates generally to oxidation of ammonia using electrochemistry. Methods and systems may include at least one sensor to measure the concentration of ammonia in the effluent and/or the concentration of chlorine gas in the effluent. Methods and systems may also include at least one controller in communication with the sensor and/or the anode to reduce the current density of the anode generating the chlorine, and/or to change the flow rate of the ammonia containing water entering the reactor.

The present disclosure relates to a bacterium-alga coupled sewage treatment device based on energy recycling and a use method thereof. The device comprises a pretreatment device, a photobioreactor, an alga separation apparatus, a continuous flow bioreactor and a secondary sedimentation tank which are sequentially connected in order, the pretreatment device being connected to a municipal sewage inlet pipe, the photobioreactor being connected to a carbon dioxide gas charging device through a gas filling pipeline, one part of a sludge thickening tank being connected to the secondary sedimentation tank, the other part thereof being connected to remaining sludge of the pretreatment device, carbon dioxide generated from the sludge which flows through the thickening tank and is thermally-hydrolyzed and anaerobically-acidified being connected to the photobioreactor through a gas inlet pipeline, and the alga separation apparatus being further connected to a filter press. The present disclosure has the advantages of a rational structural design, reliable and stable operation, a low operation and maintenance cost and high automaticity and intelligence, and being suitable for the use and transformation requirements of a wide range of sewage treatment plants, etc.

A water treatment system includes a plurality of tanks; a plurality of blow systems connected to tanks respectively; a blower unit configured to supply air to tanks through the air blow systems; and a control device configured to: calculate a pressure loss in each of the air blow systems; and control supply of water to be treated to each of the tanks, according to the calculated pressure losses.