Pakka Shift From ‘Individual Loop Control To Process-Wide Optimization’

In an exclusive interview with Paper Mart, Mr. Jagjeet Singh Khanna, Paper Machine Head, Pakka Limited, shares that traditionally, paper mills depended on operator intervention and PID-based feedback control during the paper production process. Today, the focus is on predictive and integrated control, feedforward strategies, and real-time process data to identify and correct disturbances before they impact product quality. Pakka, with its integration of DCS, QCS, APC and process data, is positioned well to take faster corrective action and achieve more stable operation. Looking ahead, the company prioritises investment in reliable instrumentation, online quality measurement, and control systems to improve machine efficiency and profile stability, reduce fibre giveaway, and enhance overall reel quality.

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.

Jagjeet Singh Khanna: The highest process variability is typically seen in stock preparation (freeness, consistency and furnish mix), wet-end chemistry and ash control, headbox consistency, and CD/MD basis weight and moisture profiles across the paper machine. Variations in these areas directly impact formation, basis weight, moisture, caliper, strength properties, reel quality and overall machine runnability.

The greatest measurable gains usually come from tighter control of headbox consistency, CD basis weight profile and final moisture profile. These parameters have a direct influence on fibre consumption, steam usage, product quality and customer specifications. In most mills, even a 1–2% reduction in basis-weight or moisture variability (2σ variation) can translate into meaningful reductions in fibre, energy and chemical giveaway while improving quality consistency and reducing off-spec production.

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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.

JSK: Traditionally, mills used to depend on operator intervention and PID-based feedback control. Today, the focus is on predictive and integrated control using APC, MPC, feedforward strategies and real-time process data.

Instead of reacting to variability, mills are increasingly predicting and correcting disturbances before they impact quality. For example, QCS, DCS and APC systems now work together to optimize basis weight, moisture and dryer performance in real time, resulting in improved stability and consistency. This shift from individual loop control to process-wide optimization is delivering significantly better process stability and product quality.

PM: How are Advanced Process Control and Model Predictive Control being applied to reduce process variability? Where have they delivered measurable gains?

JSK: APC and MPC are being used primarily for basis weight and moisture profile control, refining optimization, headbox stability, and steam-condensate management. The most visible benefits are tighter CD/MD profiles, improved grade-change performance and lower break frequency. Mills typically see 20–40% reduction in profile variability along with measurable steam and energy savings.

PM: What practical role are AI and machine learning playing in predicting and correcting process deviations before they affect production or quality?

JSK: Today, AI is creating value mainly through break prediction, predictive maintenance, anomaly detection and soft-sensor based quality prediction. Its biggest contribution is providing early warning of developing process deviations, giving operators and APC systems time to take corrective action before quality or production is affected.

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 remain?

JSK: Modern QCS scanners, machine vision systems, and smart instruments provide real-time measurements of basis weight, moisture, caliper, consistency, and ash, enabling faster correction of process variations and tighter quality control. Key gaps remain in online measurement of fibre quality, strength properties, furnish composition, and wet-end chemistry, where mills still rely heavily on laboratory testing and inferred values.

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?

JSK: Modern drive systems have significantly improved machine stability through better speed synchronisation, tension control and sectional coordination. This has helped reduce sheet breaks, tension-related defects and variability during grade changes, particularly on higher-speed machines. In addition, drive diagnostics provide early warning of mechanical issues before they impact production.

PM: How are automation, advanced control, and integrated process data helping mills achieve more consistent paper quality? Which quality parameters have benefited most?

JSK: The biggest improvements have been in basis weight, moisture, caliper and ash consistency. Integration of DCS, QCS, APC and process data enables faster corrective action and more stable operation. In practice, tighter CD basis weight and moisture profiles deliver the most visible quality improvements and material savings.

PM: As control systems become more intelligent, how is the operator’s role in process control and decision-making changing?

JSK: The operator’s role is increasingly shifting from routine control to process supervision and exception management. As APC and automation handle normal process variations, operators can focus more on troubleshooting, grade changes, abnormal situations and continuous improvement initiatives.

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.

JSK: The biggest barriers are usually poor data quality, ageing instrumentation, inadequate sensor maintenance, fragmented systems and limited in-house expertise. Most mills do not face an automation gap; they face a measurement and data-quality gap. APC and AI can only be as effective as the quality of the data feeding them.

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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?

JSK: One of the most impactful improvements in our mill was the installation of an automatic headbox integrated with the QCS system. This enabled continuous correction of basis weight variations and tighter CD profile control based on real-time measurements.

After implementation, we achieved a 20–25% reduction in CD basis weight variation, a 2–3% improvement in machine efficiency, and a noticeable reduction in off-spec production and operator interventions. Improved profile stability also helped reduce fibre giveaway and enhance overall reel quality.

Looking ahead, the mills should prioritise investment in reliable instrumentation, online quality measurement, and control systems. Once the measurement backbone is strong, technologies such as APC, MPC, and AI can deliver substantially greater value.