Tobacco Industry

Tobacco Industry

In the tobacco industry, the stability of certain key processing steps directly affects quality indicators. AIMS has developed an intelligent control and root cause analysis system for tobacco manufacturing. This system is built between the central control system and the MES system, forming an intelligent manufacturing layer. It enables less manpower-intensive production, reproducible quality, optimized controllable parameters, and root cause analysis for abnormal issues. This helps tobacco manufacturing enterprises solve pain points such as "operations relying on individual experience and non-global controllable parameters."

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Business Pain Points

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Quality Fluctuations

Different levels of skill, operating habits, experience, and concentration among operators lead to inconsistent quality metrics, causing product quality to vary.

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Lack of Integrated Control

Relying on manual estimation of process parameters results in quality metrics that barely meet specifications but are not optimized globally.

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Low Problem-Solving Efficiency

When anomalies occur, it is difficult to efficiently use historical data to objectively assess issues, analyze causes, and solve problems.

Solutions

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Integrated Line Control

Develop a digital twin model and an integrated optimization and predictive analysis model for the entire tobacco processing line, from raw material feeding to finished product. Based on factors such as incoming material conditions, tobacco leaf grade, material temperature, and environmental humidity, the system calculates optimal combinations of controllable process parameters to achieve better overall line control.

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Single-Process Optimization Control

Real-time data collection combined with a multi-input, multi-output predictive control model enables intelligent control of humidification and drying processes in tobacco processing. This significantly stabilizes the outlet moisture content and temperature of individual processes, greatly improving operational efficiency.

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Root Cause Analysis for Quality Issues

Using AIMS's proprietary industrial IoT platform, identify correlations between key quality indicators and production process variables. When quality indicators deteriorate, the system pinpoints the factors causing the anomaly and provides timely alarm notifications.

Customer Value

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Reduced Manual Workload

The intelligent control system has a high operational rate of 99% and continuously monitors various abnormalities, effectively reducing the need for manual supervision and operation.

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Reduced Quality Fluctuations

Through online optimization control of variables such as water addition and direct steam injection, the standard deviation of moisture content at the exit of key processes is reduced by 60%, and the deviation of the batch average moisture content from the target value is reduced by 0.1.

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Improved Consistency of Tobacco Shred Drying

By using batch correlation models, the consistency of the inlet moisture for drying is improved, making the dehydration amount between batches more uniform, and thereby enhancing the batch-to-batch consistency of sensory quality.

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Precise Control of Batch Addition Ratios

A weight prediction model is established to accurately predict the cumulative weight of the batch entering the addition (flavoring) process, effectively improving the precision and deviation of the addition (flavoring).

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Enhanced Consistency of Finished Tobacco Shred Moisture

By accurately controlling the moisture content between batches based on the impact of various variables, the deviation of the actual moisture content of the finished tobacco from the target value is reduced by 30%.

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Reduced Material Consumption

Through big data analysis, optimal parameters for processing head and tail materials are identified, reducing the weight of dried heads and tails in the drying process and minimizing tobacco shred material loss.