Abstract: This paper describes a temperature control system for biochemical processes based on fuzzy algorithms, detailing the overall system structure, control algorithm, and software implementation process. The system uses an 80C196KB microcontroller as the main controller, and fuzzy control is employed as the control algorithm. This system demonstrates excellent performance in temperature control of biochemical processes exhibiting time-varying, high inertia, strong time delay, and nonlinear characteristics. Keywords: Temperature control; Biochemical process; Fuzzy algorithm Abstract: This paper presents a temperature control system for a biochemical process based on fuzzy arithmetic. Details are described about the overall architecture, control arithmetic, and software flow chart. The 80C196KB MCU is selected as the main controller. The fuzzy controller is designed for the temperature of a biochemical process, exhibiting characteristics such as time-varying, inertia, time-delay, and nonlinearity, resulting in good control effect. Key words: temperature control; Biochemical Process; Fuzzy Arithmetic 1 Introduction The production process of biochemical products is a complex biochemical reaction process, characterized by time-varying, large inertia, strong time delay, and nonlinearity. To improve product quality and yield, many physical quantities in the production process, such as temperature, pressure, and pH, must be controlled in real time. However, since many reaction mechanisms are not fully understood, it is impossible to establish a mathematical model of the controlled object. Therefore, controllers using traditional control methods are sometimes not more efficient than manual control by experienced operators. Fuzzy control is a nonlinear control method that does not require establishing a mathematical model of the object and has a certain adaptability to time-varying conditions. In the temperature control of biochemical reactions, fuzzy technology is used to establish a fuzzy control model for the temperature of the monosodium glutamate (MSG) biochemical fermenter. This model has stronger anti-interference capabilities and robustness compared to the traditional PID system. 2. System Overall Design The overall system block diagram is shown in Figure 1. The main control object of this system is the temperature of the fermenter. Because the temperature of the fermenter rises gradually in a nonlinear manner during the MSG production process, it is necessary to control the temperature within the range required by the production process in order to improve product quality and yield. In actual production, cooling water is sprayed around the fermenter to lower the tank temperature. This control system uses an 80C196KB microcontroller as the main controller, and the control algorithm adopts fuzzy control. The opening of the cooling water valve is controlled, and the water is sprayed onto the surface of the tank through a loop pipeline for cooling. [align=center]Figure 1 System Overall Composition Block Diagram[/align] 3 Fuzzy Controller and Control Model Establishment 3.1 Principle of the Fuzzy Controller for Fermentation Tank Temperature This fuzzy controller uses a computer as the control subject. Control parameters and control rules are pre-stored in the computer. The actual temperature of the fermentation tank is obtained using the thermocouple temperature detection system. The corresponding fuzzy control table is calculated based on the membership function and fuzzy control rules. According to the changes in the measured temperature signal, the opening of the cooling water valve is controlled to adjust the water output, thereby achieving real-time control of the fermentation tank temperature. This system uses a two-dimensional fuzzy controller with two input variables: deviation E, which is the difference between the set value and the measured value; and deviation change rate EC, which is the change in deviation per unit time, EC=dE/dt. It has one output variable U, which controls the opening of the cooling water valve. 3.2 The assignment deviation E and deviation change rate EC of the membership function of variables can be represented in fuzzy language as negative large (NB), negative medium (NM), negative small (NS), negative zero (N0), positive zero (P0), positive small (PS), positive medium (PM), and positive large (PB). The output variable U can be represented in fuzzy language as the valve opening as: fully open or maximum (PB), very large (PM), large (PS), medium (0), small (NS), very small (NM), and fully closed or minimum (NB). The membership degree of each variable is set based on field operation experience. 1. Membership Function of Temperature Difference E: Let the universe of discourse for temperature difference E be E = {-6, -5, -3, -2, -1, -0, +0, +1, +2, +3, +4, +5, +6}. Based on the control accuracy requirements, eight fuzzy variables (NB, NM, NS, N0, P0, PS, PM, PB) are used to describe it. The assignment table for the EC variable is as follows: [align=center] Table 1: Assignment Table for Fuzzy Variable E[/align] 2. Membership Function of Temperature Difference Change Rate EC: Let the universe of discourse for temperature difference change rate EC be EC = {-6, -5, -3, -2, -1, 0, +1, +2, +3, +4, +5, +6}. Seven fuzzy variables (NB, NM, NS, 0, PS, PM, PB) are used to describe it. The assignment table for the EC variable is as follows: [align=center] Table 2: Assignment Table for Fuzzy Variable EC[/align] 3. Membership Function of Output Variable U Let the universe of discourse of output variable U be U={-7, -6, -5, -3, -2, -1, 0, +1, +2, +3, +4, +5, +6, +7}, and describe it using seven fuzzy quantities NB, NM, NS, 0, PS, PM, PB. The assignment table for variable U can be obtained as follows: [align=center] Table 3: Assignment Table for Fuzzy Variable U[/align] 4. Fuzzy Control Rules This system is a dual-input single-output system, and the fuzzy rule IF Ei and ECi then Ui is adopted. The fuzzy relation R is represented by R = Ei × ECi × Ui; the fuzzy inference is represented by Ui = (Ei × ECi) ∠R; the fuzzy rules can be derived from R and Ui, as shown in Table 4: [align=center] Table 4 Fuzzy Control Rules[/align] This system uses the maximum membership decision method to obtain the value of the output precision quantity U, which is then used to construct a fuzzy control lookup table and stored in the computer as the output control quantity. 3.3 Hardware Introduction of Fuzzy Controller 1. Main Controller: 80C196KB microcontroller, which has a high-performance 16-bit CPU, 8KB of on-chip program memory and a 232BYTE register RAM array, two 16-bit timers/counters, 48 ​​I/O interface lines, one serial port, one analog output channel, and eight built-in 10-bit A/D channels, which can simplify the interface circuit design. Since the control program is not larger than 8KB, the on-chip memory is sufficient. However, in order to process the acquired data, RAM needs to be expanded, which is constructed using a 6116 RAM chip. 2. The highest temperature during the manufacturing process of the temperature sensing element and transmitter is only 500°C. Nickel-chromium thermocouples with a temperature range of 0–1000°C are selected, with an output voltage of 0–50mV. This signal is relatively small, so the transmitter needs to convert it to the voltage range required by the A/D converter. The transmitter consists of a millivolt transmitter and a current/voltage transmitter. The millivolt transmitter converts the 0–50mV output from the thermocouple into a current range of 0–10mA, and the current/voltage transmitter converts the 0–10mA current output from the millivolt transmitter into a voltage range of 0–5V. Using an on-chip 10-bit A/D converter ensures that the quantization temperature error is maintained within ±0.50°C. 3. The D/A and A/D converters utilize an on-chip 8-channel 10-bit A/D converter, acquiring signals from eight temperature sensing points in a time-division multiplexing manner, which meets the required detection performance. The D/A conversion uses the AD7520, a 10-bit four-quadrant D/A converter manufactured by Analog Devices, Inc. It features a simple structure, good versatility, and flexible configuration. The output signal, after being amplified by a power amplifier, can directly control the opening of the cooling water valve. 4. The actuator uses four 50mm regulating valves, which spray cooling water onto the tank surface via a ring pipeline. Two manual water valves are also installed next to the fermentation tank for manual spraying cooling in case of system malfunctions. 3.4 Introduction to the Fuzzy Controller Software This software mainly includes a main program, parameter setting subroutines, field monitoring subroutines, temperature A/D subroutines, valve regulation subroutines, data processing subroutines, fuzzy inference subroutines, fuzzy decision-making subroutines, and a fuzzy relation R table, which is built into the ROM. The flowchart of the main program module is shown in Figure 2. [align=center]Figure 2 Main Program Module Flowchart[/align] 4 Conclusion Fuzzy control is a nonlinear control method that can achieve satisfactory control results for systems where mathematical models cannot be obtained. It solves some problems that traditional control methods cannot address, especially for controlling the temperature of biochemical processes with time-varying characteristics, large inertia, strong time delay, and nonlinearity. The innovations of this paper are: proposing a fuzzy control system for biochemical process temperature with an 80C196KB microcontroller as the main controller. This system is low-cost, reliable, and has strong anti-interference capabilities, improving product quality and yield. A practical, simple, fast-response, and high-performance query fuzzy control table program was developed, improving execution efficiency. A ring-type spray pipeline valve control system was designed, achieving significant water-saving effects and providing a new approach to replace traditional water cooling methods, possessing high application value. References: [1] Liu Fazhi, Zhao Mingfu. Application of fuzzy control technology in constant pressure water supply system of high-rise building [J], Microcomputer Information, 2005 (7): 21-23 [2] Zhang Huaguang, He Xiqin. Fuzzy adaptive control theory and its application [M]. Beijing: Beijing University of Aeronautics and Astronautics Press, 2002 [3] Li Youshan. Fuzzy control theory and its application in process control [M]. Beijing: National Defense Industry Press, 1993