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In the petrochemical industry, several licensors possess the original design technology for producing the same products worldwide. Consequently, many companies import this technology and apply it similarly. However, companies may modify their operation process based on the feed stock, market demand, and business policy.
Operational efficiency, including consistency of quality, stability of the process, and efficiency of operation, significantly impacts the rate of return for oil refining and petrochemical companies. Nevertheless, rising labor costs, difficulties in supplying and deploying field engineers, and increasing competition from rival companies require higher operational competitiveness.
To address this challenge, understanding and predicting the operating conditions of the process and the real-time process situation is crucial. However, volatility in raw materials and fluctuation in market conditions result in significant errors in operation forecasts, causing serious losses. To minimize these errors, machine learning-based software for process prediction will be developed and provided as a solution for processes with highly volatile raw materials and operational instability.
Operational efficiency, including consistency of quality, stability of the process, and efficiency of operation, significantly impacts the rate of return for oil refining and petrochemical companies. Nevertheless, rising labor costs, difficulties in supplying and deploying field engineers, and increasing competition from rival companies require higher operational competitiveness.
To address this challenge, understanding and predicting the operating conditions of the process and the real-time process situation is crucial. However, volatility in raw materials and fluctuation in market conditions result in significant errors in operation forecasts, causing serious losses. To minimize these errors, machine learning-based software for process prediction will be developed and provided as a solution for processes with highly volatile raw materials and operational instability.
In particular, Korean oil refineries are an example of a manufacturing industry with high process-instability due to their use of various crude oils compared to firms in other countries. Moreover, the petrochemical industry involves diverse raw material suppliers, and the unit price of raw material is highly volatile. It is not possible to predict future data with scientific formulas alone, making the demand for machine learning software high. The team plans to develop and provide tailor-made machine learning software to meet this demand.
Previous attempts to optimize feed stock through AI took into account the situation of the crude oil market and energy efficiency, but they did not consider the effect of the operating conditions and circumstances of the process on products. Due to the extreme complexity of many processes, physical formulas alone cannot predict various products, thus increasing the demand for machine learning software. DogWoodAI aims to meet the demand of each company by providing customized machine learning software.
Previous attempts to optimize feed stock through AI took into account the situation of the crude oil market and energy efficiency, but they did not consider the effect of the operating conditions and circumstances of the process on products. Due to the extreme complexity of many processes, physical formulas alone cannot predict various products, thus increasing the demand for machine learning software. DogWoodAI aims to meet the demand of each company by providing customized machine learning software.