Knowledge reuse-based method and system for predicting cell concentration in fermentation process

A knowledge reuse-based method for predicting a cell concentration in a fermentation process, comprising:

• S1:
• constructing a cell concentration soft sensor universal model in a fermentation process, wherein the fermentation process is divided into four stages in time order:
• a lag phase, an exponential growth phase, a stationary phase, and a decline phase, and for two stages that occur successively, it is defined that a former stage is a fermentation stage A and a latter stage is a fermentation stage B;
• S2:
• acquiring and preprocessing process data of the fermentation stage A;
• S3:
• determining a cell concentration soft sensor model of the fermentation stage A based on the cell concentration soft sensor universal model in combination with a process data result of the fermentation stage A after the preprocessing;
• S4:
• acquiring process data of the fermentation stage B, and after preprocessing, designing a cell concentration online soft sensor of the fermentation stage B with the cell concentration soft sensor model of the fermentation stage A;
• and
• S5:
• predicting a cell concentration of the fermentation stage B according to the cell concentration online soft sensor of the fermentation stage B,
• wherein a method for designing a cell concentration online soft sensor of the fermentation stage B in step S4 is:
• S41:
• setting a parameter estimation of the cell concentration soft sensor model of the fermentation stage B to:
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• wherein at a moment k+τ+1, for the cell concentration soft sensor model of the fermentation stage B, a delay is τB=τA, orders are pB=pA and qB=qA, the parameter estimation is {circumflex over (θ)}k+τ+1=[â, ⋅ ⋅ ⋅ , âpB, {circumflex over (b)}1, ⋅ ⋅ ⋅ , {circumflex over (b)}qB]T, â1, ⋅ ⋅ ⋅ , âpB, {circumflex over (b)}1, ⋅ ⋅ ⋅ , {circumflex over (b)}qB is an estimate of each parameter in the parameter vector of the fermentation stage B at the moment k+τ+1, where a is a parameter of a autoregressive model and b is a parameter of a moving model, {circumflex over (θ)}A is a parameter estimation of the fermentation stage A, Ĥk+τ+1 is a gain matrix of the cell concentration soft sensor model of the fermentation stage B at the moment k+τ+1, Ek+τ+1 is an innovation vector at the moment k+τ+1, Yk+τ+1 is a cell concentration matrix of the fermentation stage B at the moment k+τ+1, and Xk+τ+1 is an input matrix of the fermentation stage B at the moment k+τ+1;
• S42:
• calculating the gain matrix Ĥk+τ+1 of the cell concentration soft sensor model of the fermentation stage B in step S41;
• S43:
• designing the cell concentration online soft sensor of the fermentation stage B based on a parameter estimation vector {circumflex over (θ)}k+τ+1 and the gain matrix Ĥk+τ+1 of the cell concentration soft sensor model of the fermentation stage B.