During the summer, we embarked on a project aimed at optimizing the technological process in the chemical production of fertilizers. Our efforts included a comprehensive analysis of the existing technological procedures, the construction of a process model utilizing neural networks, and the completion of the optimization phase. Following this, the next step was to implement these changes on the production line. Operators were instructed to adhere to the recommendations provided by our digital advisor and maintain a log of the modifications made.
After a week of rigorous testing, our operators composed a letter containing their feedback on the performance of our software. While our colleagues were generally pleased, they were puzzled by one particular detail — the vacuum crystallizer model. The recommended rotation speed for the mixer displayed as 0, suggesting that the entire crystallizer needed to be taken out of operation. Upon receiving this information, our technologists speculated that our model might indeed be correct and that the crystallizer required cleaning due to excessive fouling. As it turned out, the crystallizer was significantly overgrown and urgently needed cleaning.
It became apparent that our optimizer had been trained on historical data, essentially learning from past occurrences of a similar situation. It had detected this pattern, analyzed it, and provided operators with the necessary recommendations. In essence, we had not just an algorithm but a genuine marvel: our optimizer enabled us to establish a predictor for fouling issues. Ultimately, our customer was highly satisfied with the results.
After a week of rigorous testing, our operators composed a letter containing their feedback on the performance of our software. While our colleagues were generally pleased, they were puzzled by one particular detail — the vacuum crystallizer model. The recommended rotation speed for the mixer displayed as 0, suggesting that the entire crystallizer needed to be taken out of operation. Upon receiving this information, our technologists speculated that our model might indeed be correct and that the crystallizer required cleaning due to excessive fouling. As it turned out, the crystallizer was significantly overgrown and urgently needed cleaning.
It became apparent that our optimizer had been trained on historical data, essentially learning from past occurrences of a similar situation. It had detected this pattern, analyzed it, and provided operators with the necessary recommendations. In essence, we had not just an algorithm but a genuine marvel: our optimizer enabled us to establish a predictor for fouling issues. Ultimately, our customer was highly satisfied with the results.