AI-based Process Control c/o ARMAC bv

Radboud University (together with Radboudumc part of Stichting Katholieke Universiteit) located in Nijmegen, the Netherlands, is a leading university in the Netherlands. Radboud Dept. of Analytical Chemistry is the oldest and largest academic research group exclusively focussing on data science and AI in an (analytical) chemistry context. It has a long history in finding solutions for industry regarding the analysis, control and monitoring, and optimization of their production processes.

ARMAC B.V. is an SME that deals with automation systems for large and small users, on different sectors. SVP is a heat distribution company that manages the central production and distribution of heat in the town of Purmerend; it operates 3 plants, one biomass (main) and two gas (back-up and for peak demands).

 

The objective in this experiment was to test AI solutions for a customer of ARMAC B.V. and to implement this solution in the operational control environment delivered and maintained by ARMAC B.V. In this way ARMAC gains experience in applying AI solutions for their customers. The customer is SVP, which is a district heating (DH) system of the city of Purmerend. The test involved the optimization of the district heating (DH) system through the application of AI using weather forecast information at the city of Purmerend; currently the weather is used by operators to estimate the heat demand. The system is not driven by the supply, but the demand of customers for heat. The heat demand is estimated from the operator expertise, that use previous experience, the weather conditions, the day of week and the season, as basis for decision making. However, this kind of control is non-optimal, and might be inaccurate, mainly because the weather conditions can vary greatly in a short period of time, affecting the heating demand, and leading to heat loss. The adoption of AI comes to overcome this problem. The AI has been applied to forecast the heat demand based on the weather conditions and forecast information at the city of Purmerend. IThe energy efficiency has increased after the adoption of AI, helping the operators along the decision making on heating demand, saving energy and keeping the system more efficient.

 

The experiment was conducted by the Radboud University as the experiment leader, Armac as the technology provider and the DH at Purmerend as the experimental site.

The historical data available is from about 10 years of operation. It includes the temperature, pressures, heating demand and weather conditions from 35000 dwellings. To better forecast the heat demand, this project has applied AI technologies using weather conditions and weather forecast information at the city of Purmerend. The following technologies have been employed: deep learning, supervised learning, and expert systems. The experiment was divided in four main phases, data collection, data modelling, model learning, model validation and deployment.