Methods and systems for collaborative prediction and intelligent control of multiple pollutants in waste incineration flue gas

A method for collaborative prediction and intelligent control of multiple pollutants in waste incineration flue gas, comprising:

• S1, collecting all historical data of operating parameters of a waste incinerator and emission concentrations of flue gas pollutants at a set time interval within a same time period as collection samples to obtain incinerator operating parameter data and emission concentration data of the flue gas pollutants, wherein the incinerator operating parameter data is obtained from a storage database of a distributed control system (DCS) of the waste incinerator, the flue gas pollutants include four types of flue gas pollutants:
• hydrogen chloride (HCl), sulfur dioxide (SO2), nitrogen oxides (NOx), and particulate matter (PM), and the emission concentration data of each type of the flue gas pollutants is obtained from a storage database of a continuous emission monitoring system (CEMS);
• S2, for the operating parameters of the waste incinerator and the emission concentrations of the flue gas pollutants in the collection samples obtained in the step S1, calculating a Pearson correlation coefficient between the incinerator operating parameter data and the emission concentration data of each type of the flue gas pollutants;
• screening, based on the Pearson correlation coefficient between the incinerator operating parameter data and the emission concentration data of any one of the four types of flue gas pollutants, all the operating parameters of the waste incinerator in the collection samples, and retaining a screening result as input features for collaborative prediction training, wherein the emission concentration data of the four types of flue gas pollutants in the collection samples collected at a same time corresponding to the incinerator operating parameter data are used as data labels corresponding to the input features;
• S3, performing mean down-sampling with a larger time span on data screened in the step S2;
• then selecting a part of the incinerator operating parameter data from the collection samples, performing time-series processing thereon to obtain processed data, and using the processed data as input data for training;
• constructing a multi-pollutant collaborative prediction model based on a long short-term memory (LSTM) layer structure, inputting incinerator operating parameter data of a set time period to train the multi-pollutant collaborative prediction model, and using a collaborative prediction result corresponding to concentrations of the four types of flue gas pollutants after the set time period as an output of the multi-pollutant collaborative prediction model;
• S4, absorbing and treating the four types of flue gas pollutants by a flue gas purification system using a corresponding absorbent, respectively;
• aiming at controlling a dosage of each absorbent corresponding to the four types of flue gas pollutants to construct a multi-objective optimization function F(x), wherein the multi-objective optimization function F(x) is generated based on two types of objective functions:
• a cost index function considering an absorbent dosage and an environmental protection index function considering pollutant emission amounts, a final optimization objective is to minimize a value of the multi-objective optimization function F(x), and input variables of the cost index function and the environmental protection index function both use a collaborative predicted concentration of flue gas multi-pollutants output by the multi-pollutant collaborative prediction model;
• S5, setting a constraint condition for the multi-objective optimization function F(x) according to an actual dosage range of the each absorbent in engineering application and a limit standard of an emission concentration of each pollutant, using a multi-objective optimization algorithm to solve the multi-objective optimization function F(x), and calculating optimal dosage data of the each absorbent corresponding to the four types of flue gas pollutants, respectively, as a calculation result;
• and
• S6, inputting the calculation result of the step S5 into the DCS of the waste incinerator, converting the optimal dosage data of the each absorbent corresponding to the four types of flue gas pollutants into an actual analog control signal, and then transmitting the actual analog control signal to a dispensing valve of the each absorbent in the flue gas purification system, wherein by controlling an opening degree of the dispensing valve of the each absorbent, feedback regulation of the dosage of the each absorbent is achieved, ensuring that the flue gas pollutants meet environmental emission standards while reducing the absorbent dosage, thereby achieving a cost-economic objective of the flue gas purification system.