The final products of a chemical reactor are affected by various properties including internal temperature, pressure and life-time of catalyst. External temperature and moisture are also important factors when external air is infused in the reactor. INEEJI’s AI Guidance Predict learns the AI models with variables inside and outside of the reactors. The AI prediction system accurately predicts important target variables including amount of the product, selectivity rate and conversion rate. This AI prediction makes it possible to operate the chemical reactors in the optimal condition.
Boiler of Power Plant
A boiler of coal power plant is affected by the types of coals and characteristics of incombustible coal ash. The ash eventually is built up as a clinker which prevents the heat exchange between the air in the boiler and the water in the tube. Monitoring the internal status of boiler is also important since the changes in the boiler may change the amount of pollution such as Sulfur Oxide (SOx) and Nitrogen Oxide (NOx). INEEJI's AI Guidance Predict predicts the changes of thousands of temperature sensors simultaneously with operational variables (e.g., electricity generation, coal types) and sensor inputs such as temperature, pressure and flow. Thus, electricity generation can be maximized with reduced pollution.
Electric Arc Furnace
The efficiency of electric furnace will be affected by the quantity and type of scrap. Melting scrap can be expensive when excessive amount of electricity is required to melting the scrap. INEEJI’s AI Guidance Predict learns an AI model to predict the required amount of total energy (electricity) by considering the type and amount of scrap, oxygen provided, internal temperature and consumed electricity. Thus, the proper amount of electricity is calculated and predicted real-time to prevent excessive electricity is provided. Customers could save expensive electricity bills by maintaining the high quality of product.