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Industrial process modeling is crucial for understanding and optimizing manufacturing operations. This approach generates valuable insights and improves efficiency by leveraging site data to make accurate predictions and informed decisions. However, many companies do not fully utilize their data, leading to less reliable business decisions.
The goal of data-driven process modeling is to develop robust models that enhance decision-making and operational efficiency. These models use advanced methodologies like machine learning to analyze complex data patterns, predict outcomes, and optimize processes.
In essence, data-driven industrial process modeling transforms raw data into actionable insights, enabling better decisions and improved efficiency in manufacturing.
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CASE STUDY
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A high-end jewellery manufacturer required a new design for a batch incinerator to recover gold particles in the ash content of the waste material being incinerated.
Our team were tasked to design the entire plant and focus on reducing intensive airflow during the combustion process to avoid excessive dust emissions.
Through complex fluid simulation, the batch incinerator was designed to minimise the gold particles being released in the ductwork and impregnate the insulation materials.
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If you’re interested in learning more about our industrial engineering services and products, or if you need help with a specific project, please contact us.