As paper manufacturing becomes automated and data-driven, maintaining process stability is critical to achieving consistent quality, higher efficiency, and lower operating costs. In an exclusive interview with Paper Mart, Mr. Pradip Patel, Deputy General Manager, Jani Sales Private Limited, discusses how paper mills are moving from reactive to more proactive and predictive operations to reduce process variability across stock preparation, the wet end, press section, and dryer section. He emphasises the role of Advanced Process Control (APC), Model Predictive Control (MPC), AI-based analytics, Machine Learning (ML), and predictive maintenance. He also highlights the mill’s implementation of an online chemical dosing monitoring system to optimise chemical consumption and maintain stable paper quality, while noting that improving online measurement in key processes will be the next step towards achieving even greater process stability.
Paper Mart: Where does process variability remain most persistent in paper manufacturing today, and how does it affect quality and operating performance? Please tell us the process stages or parameters where tighter stability can deliver the greatest measurable gains.
Pradip Patel: Process variability is most persistent in stock preparation, the wet end, the press section, and the dryer section. Key parameters include pulp consistency, refining, basis weight, moisture, pH, retention, and steam pressure. These variations affect paper quality, machine runnability, energy consumption, and overall production efficiency. By improving process stability through advanced automation, APC/MPC, and real-time monitoring, mills can reduce breaks and waste, improve quality consistency, lower operating costs, and increase overall productivity.
PM: How has the approach to controlling process variability changed in recent years? In other words, how are mills moving beyond conventional automation and feedback control to achieve greater process stability? Please explain with examples.
PP: Earlier, paper mills mainly relied on conventional PID and feedback control, which corrected problems only after they occurred. Today, the approach is much more proactive. Mills are using technologies such as Advanced Process Control (APC), Model Predictive Control (MPC), AI-based analytics, and predictive maintenance to identify process changes early and make automatic adjustments before they impact quality. For example, MPC can control basis weight and moisture simultaneously, while AI can predict sheet breaks or equipment issues in advance. This helps improve process stability, reduce waste, increase machine efficiency, and maintain consistent product quality.
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PM: How are Advanced Process Control and Model Predictive Control being applied to reduce process variability? Where have they delivered measurable gains?
PP: Advanced Process Control (APC) and Model Predictive Control (MPC) help reduce process variability by controlling multiple process variables simultaneously and making adjustments before deviations occur. In paper mills, they are commonly used for basis weight, moisture, refining, and steam pressure control. This results in more consistent paper quality, fewer sheet breaks, lower energy and chemical consumption, and higher machine efficiency. Many mills have achieved measurable improvements such as reduced quality variation, increased production, and lower operating costs after implementing APC and MPC.
PM: What practical role are AI and machine learning playing in predicting and correcting process deviations before they affect production or quality?
PP: Artificial Intelligence (AI) and Machine Learning (ML) are helping paper mills move from reactive to predictive operations. They analyse real-time process data to identify patterns that may lead to quality issues, sheet breaks, or equipment failures. Instead of waiting for a problem to occur, AI provides early warnings and recommends or automatically makes process adjustments. For example, it can predict felt or bearing failures, optimise chemical dosing, or detect variations in moisture and basis weight before they affect production. This helps improve product quality, reduce downtime, and increase overall process stability.
PM: How are online measurement, smart instrumentation, machine vision, and QCS improving real-time detection and control of process variation? Where do significant measurement or data gaps still remain?
PP: Today, paper mills use online sensors, QCS (Quality Control System), smart instruments, and machine vision to monitor important parameters such as basis weight, moisture, caliper, ash content, sheet defects, and paper profile in real time. This allows operators and control systems to detect process variations immediately and make automatic corrections to improve paper quality and reduce waste. However, some gaps still remain, particularly in the online measurement of fibre quality, refining efficiency, wet-end chemistry, and sheet formation. These areas still rely on laboratory testing or operator experience. Therefore, improving online measurement in these processes is the next step towards achieving even better process stability.
PM: Drives and motion control directly influence speed, tension, synchronisation, and machine stability. How are newer drive and control technologies helping mills reduce variability across the paper machine and associated processes?
PP: Modern drive and motion control systems provide more accurate speed, tension, and synchronisation across the paper machine. Compared to older systems, they respond faster to process changes and maintain stable operation even at high machine speeds. Features such as servo drives, vector control, and integrated automation help reduce sheet breaks, improve reel quality, maintain consistent tension, and increase overall machine efficiency. This results in better product quality, less downtime, and more stable production.
PM: How are automation, advanced control, and integrated process data helping mills achieve more consistent paper quality? Which quality parameters have benefited most?
PP: Automation, advanced process control, and integrated process data help mills maintain stable operating conditions by continuously monitoring the process and making real-time adjustments. This reduces process variation and improves product consistency. The quality parameters that benefit the most include basis weight, moisture, caliper, ash content, formation, thickness profile, and reel quality. As a result, mills achieve fewer quality complaints, lower waste, and more consistent paper production.
Artificial Intelligence (AI) and Machine Learning (ML) are helping paper mills move from reactive to predictive operations. They analyse real-time process data to identify patterns that may lead to quality issues, sheet breaks, or equipment failures.
PM: As control systems become more intelligent, how is the operator’s role in process control and decision-making changing?
PP: As control systems become more intelligent, the operator’s role is shifting from manual process control to process supervision and decision-making. Instead of constantly adjusting machine settings, operators monitor real-time dashboards, analyse alarms and AI recommendations, and focus on optimising production. Automation handles routine control tasks, while operators use their experience to solve complex problems, improve performance, and make strategic decisions. This leads to safer operations, greater efficiency, and more consistent product quality.
PM: What typically prevents a mill from achieving the full benefits of advanced automation and intelligent control? Please tell us more about issues such as legacy systems, data quality, system integration, instrumentation, workforce capabilities, etc.
PP: The biggest challenges are legacy control systems, poor-quality or missing process data, outdated instrumentation, and a lack of integration between DCS, QCS, and other plant systems. In many mills, sensors are not accurate or properly maintained, which affects automation performance. Another challenge is workforce readiness—operators and maintenance teams need training to effectively use advanced automation and AI tools. To achieve the full benefits, mills need reliable instrumentation, clean and integrated data, modern control systems, and skilled personnel who can work with these technologies.
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PM: Can you share a recent implementation that measurably reduced variability or improved quality consistency? What results were achieved, and where should mills prioritise investment next?
PP: In our mill, we implemented an online chemical dosing monitoring system. We continuously monitor chemical consumption and process parameters in real time. Using historical trend data for each paper grade, we optimise the chemical dosage instead of using fixed set points. This has reduced chemical consumption, improved dosing consistency, and maintained stable paper quality. Going forward, I believe mills should invest more in AI-based optimisation and predictive analytics to further improve process stability and reduce operating costs.
Going forward, I believe mills should invest more in AI-based optimisation and predictive analytics to further improve process stability and reduce operating costs.
