Intelligent modeling method of scr reaction process of diesel vehicle exhaust

An intelligent modeling method of a Selective Catalytic Reduction (SCR) reaction process of diesel vehicle exhaust, comprising:

• S1:
• acquiring a time sequence data set of an SCR reaction process of diesel vehicle exhaust for modeling, wherein the time sequence data set comprises an input variable sequence and an output variable sequence;
• and
• S2:
• selecting a multi-layer type-II fuzzy neural network as an SCR reaction process model of diesel vehicle exhaust based on the input variable sequence and the output variable sequence obtained in S1;
• wherein establishing the SCR reaction process model of diesel vehicle exhaust in the Step S2 comprises the following steps:
• A1:
• selecting a type-II fuzzy membership function parameter and a fuzzy subsequent layer membership function, selecting a descent algorithm, and determining the number of nodes of a multi-layer structure;
• A2:
• identifying antecedent parameters of a type-II fuzzy neural network model in an SCR reaction interval of diesel vehicle exhaust using a gradient descent method;
• and
• A3:
• identifying antecedent parameters of the type-II fuzzy neural network model in the SCR reaction interval of diesel vehicle exhaust using a recursive least square method;
• wherein the input variable sequence in the Step S1 comprises one or more of an NOx concentration at an SCR inlet, a urea injection amount, an NH3 escape concentration after SCR and an SCR catalyst temperature;
• the output variable sequence in the Step S1 comprises an NOx concentration at an SCR outlet;
• and
• the multi-layer type-II fuzzy neural network model in the Step S2 comprises an input layer, a type-II fuzzy layer, an interval operation layer, a fuzzy subsequent layer, a descent layer and an output layer.