38 results about "Sludge volume index" patented technology

A sludge bulking fault identification method based on a recursive RBF neural network belongs to the field of control and also belongs to the field of water treatment. Aiming at the problems that sludge bulking is difficult to accurately detect and the cause of sludge bulking is difficult to identify in the process of sewage treatment, the present invention designs a sludge bulking fault identification method based on recursive RBF neural network. The soft-sensing model of the sludge volume index SVI completes the real-time prediction of the sludge volume index SVI concentration. Once the occurrence of sludge bulking is detected, the fault variable identification CVI algorithm is used to accurately identify the fault variables that cause sludge bulking. The method can control the sewage treatment process in advance according to the fault variable, reducing the occurrence rate of sludge bulking.

The invention discloses a sludge volume index (SVI) soft measuring method based on a self-organized T-S fuzzy nerve network and belongs to both the field of control and the field of sewage treatment. The accurate prediction of an SVI is the guarantee for normal operation of a sewage treatment process. The method comprises: first of all, taking the output quantity of a rule layer, i.e., the space activation intensity of the rule layer as a basis for determining whether a fuzzy rule is increased; secondly, on the basis of generating a new fuzzy rule, taking the output quantity of a membership function layer as a basis for determining whether a fuzzy set is increased; and finally obtaining a self-organized T-S fuzzy recursion nerve network by using a gradient decrease algorithm to adjust the weight value parameter of a model and the center value and width of a Gauss function, and establishing an SVI on-line soft measuring model based on an SOTSFEN such that real-time detection of the SVI is realized, and an effective method is provided for preventing sludge expansion.

A soft measurement method for an SVI (sludge volume index) belongs to the field of water treatment. Production conditions of sewage treatment process are severe, random disturbance of the sewage treatment process is serious, and the sewage treatment process is high in nonlinearity, large in time varying and serious in lagging. Almost all municipal sewage treatment plants and most industrial sewage treatment plants in China have sludge bulking to varying degrees every year, sludge bulking is mainly characterized by deterioration of sludge settleability, and the SVI is a parameter indicating the sludge settleability, but the key index is difficult to be measured on line. The soft measurement method based on a self-organizing RBF (radial basis function) neural network aims at solving the problem that the SVI cannot be measured on line in the sewage treatment process, the self-organizing RBF neural network is used for measuring the SVI on line in the sewage treatment process, normal operation of sewage treatment is ensured, cost is reduced, timely monitoring parameters are provided for realizing closed-loop control in the sewage treatment process, and the sewage treatment plants are promoted to run efficiently and stably.

The invention relates to a method for forecasting sludge volume index in the sewage treatment process and belongs to the field of sewage treatment. The sewage treatment process is severe in production condition and serious in random interference and has the features of being strong in nonlinearity, large in time varying and severe in hysteresis. Sludge volume of different degrees exists in almost all of the municipal sewage plants and most of industrial sewage treatment plants in China every year. The main features of the sludge volume are that sludge settleability is worsened, the sludge volume index (SVI) represents the parameter of the sludge settleability, but the SVI which is criticality index is difficultly measured online. In order to solve the problem that the SVI in the sewage treatment process cannot be measured online, the forecasting method based on an integrated nerve network is adopted to achieve real-time forecasting of the SVI in the sewage treatment process, and good effect is obtained.

The invention discloses a method for inhibiting activated sludge bulking and belongs to the technical field of sludge preparation. A purely natural plant extract solution added with copper sulfate, magnesium chloride and sodium chloride is adopted to inhibit bacterium growth, then aeration treatment is performed, the compactness of sludge is increased, and the settling property of the sludge is improved, so that the method for inhibiting activated sludge bulking is obtained. Examples prove that the method is simple to operate and easy to implement, the settling property is improved, growth of bacteria is effectively inhibited, so that sludge particles are hardened and cannot lose easily, the finally measured SVI (sludge volume index) is decreased from original 120-150 mL / g to 80-100 mL / g, SV is dropped from 50%-55% to 10%-12%, and bulking disappears gradually.

The present invention proposes a sludge bulking suppression method based on self-adaptive segmental sliding mode control, aiming at the problem that abnormal sludge bulking is easy to occur in the sewage treatment process, and it is difficult to transition to normal working conditions, and realizes the suppression of sludge bulking . The present invention adopts fuzzy neural network to obtain water quality parameters in the sewage treatment operation process to predict the sludge volume index in real time to judge the working conditions of the operation process, provide reference signals for the switching of the controller, and design a segmented sliding mode control with an adaptive switching mechanism The device regulates the dissolved oxygen concentration and nitrate nitrogen concentration, coordinates the biochemical reaction process, improves the sludge settling characteristics, and can ensure that the sewage treatment process returns to normal working conditions when sludge bulking occurs.

The invention relates to a method for rapidly solving sticky sludge bulking problem and belongs to the field of biochemical wastewater treatment. For a sticky sludge bulking problem caused by a high organic load, a means that inflow water supply is diluted and an operation mode of a reactor is changed is adopted, so that a system operates under the conditions of low inflow water organic load and high substrate concentration gradient; the sludge settleability is controlled in real time and conditions are improved in real time by detecting parameters such as a sludge volume index, an extracellular polymeric substance and a Zeta potential in an operation process. Practice shows that the sludge settleability can be obviously improved by continuously operating the method for rapidly solving the sticky sludge bulking problem for 2-8 days; in a later operation process, recurrence of the sticky sludge bulking problem can be effectively prevented by improving an inflow water and operation mode of a system. The method for rapidly solving the sticky sludge bulking problem has the advantages of short period, low recurrence rate, low economic cost and small influence on sludge activity and can be taken as an alternative scheme for rapidly and efficiently solving the sticky sludge bulking problem in an activated sludge process system.

A method for building a filamentous bacterium SVI (sludge volume index) characteristic model is an important branch of the technical field of advanced manufacture, and is also an important part of the field of water treatment. In order to solve the problems that filamentous bacterium sludge expansion is caused by numerous factors and a mechanism model is difficultly built, based on filamentous bacterium sludge expansion causation factor analysis, dynamic characteristics of filamentous bacterium growth are extracted, and model parameters are corrected by a data statistical method. An SVI is predicted by the aid of relevant process variables and a filamentous bacterium sludge expansion mechanism, the problem of difficulty in building a sludge expansion model is solved, adaptability of the model to environmental differences in the sewage treatment process is improved, and monitoring of abnormal conditions of the sewage treatment process is guaranteed. Experimental results indicate that the model can rapidly and effectively predict the SVI, is high in prediction accuracy and has excellent adaptability to the environmental differences, and efficient stable operation and monitoring of the abnormal conditions of the sewage treatment process are guaranteed.