Lysozyme fermentation temperature control method based on LSTM-PID

• Main Authors: SONG LIHAO, XI XUGANG, PAN HONGTAO, LI XUNGEN, XU XIAOLIANG, YOU BIN, ZHOU HANLIN, LI WENJUN
• Format: Patent
• Language: Chinese; English
• Published: 14.04.2023
• Subjects: BEER; BIOCHEMISTRY; CALCULATING; CHEMISTRY; COMPOSITIONS OR TEST PAPERS THEREFOR; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL ORENZYMOLOGICAL PROCESSES; CONTROLLING; COUNTING; ELECTRIC DIGITAL DATA PROCESSING; ENZYMOLOGY; INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS; MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEICACIDS OR MICROORGANISMS; METALLURGY; MICROBIOLOGY; MUTATION OR GENETIC ENGINEERING; PHYSICS; PROCESSES OF PREPARING SUCH COMPOSITIONS; REGULATING; SPIRITS; SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES; VINEGAR; WINE

The invention discloses a lysozyme fermentation temperature control method based on LSTM-PID, and the method comprises the steps: building a temperature mathematical model of a lysozyme fermentation tank through a mechanism modeling method, and improving the optimization capability of a whale optimization algorithm through a random swimming strategy and a Levy flight strategy. And then determining initial stage parameters for the LSTM by using the improved whale optimization algorithm, setting the output of the LSTM neural network as three K values of a PID controller, training to obtain the most appropriate K value, substituting the K value into the PID controller to obtain control parameters, and transmitting the control parameters to a fermentation tank production control system for temperature control. The whale algorithm is optimized by using a Levy flight and random swimming strategy, and the problem that the whale optimization algorithm is easy to fall into a local extremum, namely, premature convergen