Manipulating and monitoring the variables of temperature dependent systems can be a very complex task for most industrial facilities since they require either the close attention of experienced engineers or highly expensive control programs. These systems are often poorly operated, which increases the cost of production and affects the overall performance of the process. This paper aims at proposing a solution to this problem using adaptable Model Predictive Control (MPC) algorithms for temperature dependent systems and computational methods to optimize their performance, while maintaining a stable temperature within the process. This research investigates and evaluates MPC and compares its performance to manual procedures for controlling temperature dependent systems. The method being investigated approximates future output process values like chemical concentrations in order to determine accurate set point changes to input variables that keep them at their desired targets. In addition, the algorithms in this program match predetermined temperature patterns that indicate if the input variables of the system are correctly balanced for operating at the desired production rate. Balance is achieved by using PID closed-loop control procedures on the output variables, as well as data storage algorithms to help reduce the error of future set point computation
