This study proposes an adaptive proportional–integral–derivative (PID) controller for an industrial heating system to improve temperature-tracking performance under varying setpoints and thermal load disturbances. Unlike a conventional PID controller with fixed gains, the proposed method updates the proportional, integral, and derivative gains (K_p​, K_i​, and K_d) online based on the temperature error and its rate of change. The system was simulated for 6,000 s using multiple temperature setpoints ranging from 50 to 100 °C and three thermal load disturbance intervals, with a conventional PID controller used as the benchmark. Control performance was evaluated using the mean absolute error (MAE), root mean square error (RMSE), integral absolute error (IAE), integral squared error (ISE), overshoot, settling time, and steady-state error. The results showed that the adaptive PID controller reduced MAE by 34.90%, RMSE by 23.86%, IAE by 34.90%, and ISE by 42.03% compared with the conventional PID controller. The average steady-state error decreased from 6.251 to 3.081 °C, corresponding to a 50.72% improvement. The adaptive controller also maintained the temperature closer to the setpoint and demonstrated better recovery from thermal load disturbances. However, improvements in overshoot and settling time were not consistent across all setpoint changes because of thermal inertia and heating-power limitations. Overall, the results demonstrate that a simple error-based gain adaptation strategy can enhance temperature-control performance while retaining the practical and straightforward structure of conventional PID control.