Abstract: Serious lag and nonlinearity have been found in the temperature control of the livestock bioliquefaction device. Existing landfill, incineration, and chemical treatments cannot fully meet the large-scale production in recent years, such as secondary pollution, high costs, and long cycles. Fortunately, the bioliquefaction technology can also degrade them into high-value liquid amino acid fertilizers. Among them, temperature is one of the key parameters- emulsification sterilization is required for a constant temperature of 100°C for 30 min, while fermentation requires a constant temperature of 35°C for 24 h. However, the conventional PID control is difficult to cope with the insufficient dynamic and steady-state accuracy of the system. Therefore, this study aims to construct an efficient temperature control scheme for the harmless and resourceful processing of livestock wastes. An intelligent control strategy was also proposed using the wild horse optimization (WHO) and BP neural network PID (BPNN-PID). Specifically, the reactor contained a reaction vessel, a stirrer, and a jacket layer. The power of the heating rod was controlled by a controllable silicon voltage regulator, and the cooling control was adjusted by the cold-water valve. In hardware, the ST20 CPU module of the Siemens S7-200 Smart PLC was combined with the EM AM06 analog input/output expansion. The OPC UA protocol was used to realize the real-time data interaction between the industrial PC and the PLC. The monitoring interface was developed to record the temperature and pH values using KinSealStudio. An "offline optimization + online adjustment" architecture was adopted: Firstly, the offline optimization of BPNN initial weight thresholds was used by the Wild Horse Optimization, in order to avoid the local optimization and slow convergence; Then, a three-layer BPNN (input layer with 3 nodes: temperature deviation, total deviation, deviation change; hidden layer with 5 nodes; output layer with 3 nodes: PID parameters K_p, K_i, and K_d) was constructed. Online adjustment of the dynamic parameter was carried out to verify the effectiveness of this strategy. An anti-integral saturation module was also added to prevent system instability. A simulation platform was established in the Matlab/Simulink platform, with the conventional PID, BPNN-PID, PSO-BPNN-PID, and GA-BPNN-PID as the controls. There was a better control performance of the BP neural network PID control algorithm using Wild Horse Optimization, when a constant step input was applied in the first 1500 s. No overshoot was observed during temperature rise, indicating the faster convergence to the target. A regulation time of 484.9 s was shorter than the conventional PID and BPNN-PID by 86.7 and 32.8 s, respectively. The overshoot was reduced by 0.11 percentage points, compared with the conventional PID. It was also shorter than the PSO and GA optimized BPNN-PID by 11.2 and 25.5 s, respectively. The fast response and small overshoot were still maintained during the cooling stage from 100 °C to 35 °C. In the temperature control experiment of the livestock bioliquefaction device, the better control performance was achieved in the BP neural network PID control algorithm using Wild Horse Optimization, with the rising time similar to PSO and GA-BPNN-PID at 58 min, the lowest overshoot of 0.66%, and a regulation time of 69 min, which was shorter than conventional PID by 45 min, and BPNN-PID by 13 min. The steady-state error was 1.56%, which was reduced by 3.12, 1.57, 1.37, and 0.31 percentage points, respectively, compared with the rest four controllers. The output was more accurately approaching the set temperature. The WHO-BPNN-PID control algorithm performed better in regulating the speed and overshoot. The large hysteresis and nonlinear of the system were effectively reduced to fully meet the temperature control requirements of the biological liquefaction of diseased and dead livestock and poultry.