Problem

With time, the buildup of debris, often known as fouling, will occur on the surface of heat exchangers. Fouling can cause drop in heat transfer efficiency and in worst cases, permanent damage. This project aims to combine first principle and machine learning models to build an accurate and efficient prediction algorithm of error occurrences for one to derive a cleaning schedule from.

Concept Generation

Amongst prediction models, there are two main types: first principle (mechanism), which are models based on theoretical principles, and data-driven, which are models built on data. We began with an initial naïve model and generated sub-system concepts that fulfill requirements.

Final concepts were chosen during the combinational process. Feasibility and efficiency were taken in consideration.

Design Description

Our algorithm is broken into five sub-functions. The basic idea behind our structure is that we initially take given parameters in Matlab and summarize the current condition to be used to predict future values and then translated to respective predicted aspects with first-principle models (if needed) to describe a future state. We determine if degradation of the system occurs if the predicted values fall below predetermined values.

Modeling and Analysis

The algorithm was implemented in Matlab. Historical data is inputted and transformed into predicted values displayed in graphical form. Predictions are made for heat transfer efficiency, outlet temperature, the heat exchanger’s fouling resistance, heat transfer amount and UA.