Optimizing and Monitoring of Nitric Acid in Milk Tank Using Machine Learning and IoT

Cleaning milk tanks with nitric acid is a common practice in the dairy industry. Nitric acid is used as a cleaning agent because of its strong acidic properties, which help to dissolve and remove organic residues, bacteria, and mineral deposits that can accumulate on the surfaces of the tanks. This cleaning process is essential for maintaining hygiene standards and ensuring the quality and safety of the milk stored in the tanks. However, it's crucial to follow proper protocols and guidelines to ensure that the nitric acid is used safely and effectively, minimizing any potential risks to both the workers and the milk product. This paper presents an integrated system designed to monitor nitric acid and measure the scale formation thickness in industrial tanks, employing waterproof ultrasonic sensors for accurate and reliable measurement. The system is specifically developed for tanks with a capacity of 5000 liters. The waterproof ultrasonic sensors enable continuous monitoring of the nitric acid level within the tank, ensuring precise data acquisition even in challenging industrial environments. Leveraging a machine learning regression model, the system predicts the required amount of nitric acid based on the observed scale formation thickness, facilitating proactive maintenance and optimization of acid usage. By combining advanced sensing technology with machine learning algorithms, the proposed system offers an efficient solution for enhancing operational efficiency and cost-effectiveness in industrial processes reliant on nitric acid. The ability to accurately monitor and control cleaning operations translates to reduced downtime, lower maintenance costs, and improved overall productivity. This cleaning process is essential for maintaining hygiene standards and ensuring the quality and safety of the milk stored in the tanks. However, it's crucial to follow proper protocols and guidelines to ensure that the nitric acid is used safely.