Optimization Of Coagulation In Wastewater Treatment: Feasibility Study Of Decision Tree Model Application

Coagulation method is one of the most commonly used pretreatment and physicochemical processes.It is widely used in industrial wastewater,municipal wastewater and natural water.Since the end of last century,coagulation method has developed greatly in the developing of new agents and combination with other processes.However,in the operation of treatment projects,the effects of high dosage,water quality fluctuation and other problems have not been solved well.Meanwhile,such limitations as reagents selection,coagulants combination and unknown parameters in optimization of removal rate in the coagulation method existing in the scope of design,these factors restrict the design of coagulation method,the accuracy and application effect.Therefore,through the optimization of the coagulation treatment of two different kinds of wastewater,the author investigates the effect of coagulation method in practice and analyzes its problems and solutions.Simultaneously,to solve the limitation of design in the coagulation process,the author puts forward the data analysis method based on Decision Tree?DT?model,through the way of Machine Learning for classifying and forecasting the designing parameters,in order to establish a preliminary study of intelligent design,meanwhile,to achieve the optimization of the existing engineering process and the design of the new project.Main research contents and results are as follows:By the cost optimization of coagulation floatation in the treatment of wool washing wastewater,through lab tests found that the using of Ca?OH?₂ as pH regulator can reduce the dosage of PAC by 37.1%.After the actual adjustment,the dosage cost was reduced by approximately 27%.The outlet COD_Cr was 122-180mg/L,SS<110mg/L,reached the requirements of the local sewage treatment plant and the water quality was stable.For the optimization of treatment of circuit board wastewater and solving the Cu²⁺concentration fluctuating.After analyzing and checking the feedback information,a feasibility experiment was conducted on the combination of Na₂S+PAC+PAM showed it could reduce the Cu²⁺by the removal rate over 97%.After increasing the precipitation time and put one more drug adding system,the coagulation sedimentation method has a good effect on all kinds of pollutants in the waste water of the circuit board,and the final outlet of Cu²⁺was less than 0.25mg/L.Based on important parameters in two kinds of practical debugging of wastewater,together with the published literature of parameters for sorting,depending on the type of data needed for DT model for data preprocessing.It was found that there are a large number of parameters in the current literature are incomplete,thus,it would have a certain impact to classification ability of the DT model.Based on the original data,through a designed data classification method for the DT classification analysis,the result shows that the DT for analyzing the parameters in the coagulation process has good classification ability,by such a method,the reference platform can be built for coagulation process design.It also shows the feasibility of DT model using in the classification and prediction of coagulation process parameters,It can optimize the coagulation process by means of data processing.Through supervised learning through Random Forest,it is found that there is a large amount of error and low sensitivity and specificity in the test set,and the fine precision of the model is shown in the training set.The reasons can be insufficient data,lack of parameters of coagulation process,subjective ambiguity of variable classification.Through the optimization of the actual project and the results of the analysis of the literature process parameters,it can be seen that the systematic coagulation process parameter big database has practical value.Its establishment can improve the ability for data analysis and establish the intelligent expert system to assist engineering design and debugging.