As paper mills focus on greater process stability and consistent quality, advanced control technologies are enabling a shift from reactive automation to proactive process management. In an exclusive interview with Paper Mart, Mr. Rajnish Gera, CEO, Trident Group, discusses how Advanced Process Control (APC), real-time process data, predictive models, AI and Machine Learning are helping mills manage variability before it affects quality. He highlights the integration of online sensors, QCS, DCS and laboratory systems to enhance visibility of critical parameters such as basis weight, moisture and ash content. Mr. Gera also explains how optimised chemical dosing and tighter process control can reduce chemical consumption and waste, improve machine efficiency, strengthen operational stability and support consistent paper quality and overall manufacturing performance.
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.
Rajnish Gera: Paper manufacturing operations often face challenges in furnishing consistent raw materials and ensuring adequate water quality and availability. These variations can significantly impact chemical dosing efficiency and wet-end chemistry performance, leading to fluctuations in critical quality parameters. Inaccurate or unstable chemical dosing can result in quality defects, yield losses, increased rejections, and higher production costs. By optimising chemical dosing and maintaining tighter control over key process parameters, mills can improve product consistency, reduce chemical consumption and waste, enhance machine efficiency, increase operational stability, and achieve superior manufacturing performance while ensuring customer satisfaction.
[bsa_pro_ad_space id=16]
Watch: Top Paper Companies 2023
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.
RG: Paper mills are increasingly adopting Advanced Process Control (APC) to move beyond traditional reactive automation and achieve greater process stability. By leveraging real-time process data, predictive models, and multi-variable control strategies, APC proactively manages process variability before it can impact product quality
Integrated with online sensors, quality measurement systems, and DCS platforms, APC optimises chemical dosing and critical quality parameters such as basis weight, moisture, and ash content. This results in improved paper quality consistency, enhanced machine runnability, reduced raw material and chemical consumption, lower operating costs, and higher production efficiency.
PM: How are Advanced Process Control and Model Predictive Control being applied to reduce process variability? Where have they delivered measurable gains?
RG: The paper industry is facing increasing challenges due to an ageing workforce, dependence on operator expertise, and legacy automation systems that primarily rely on reactive control strategies. These constraints often result in process variability, inefficient resource utilisation, and inconsistent product quality. Advanced Process Control (APC) addresses these challenges by leveraging real-time process data, predictive analytics, and multivariable control techniques to optimise chemical dosing and critical quality parameters. By proactively managing process variations and reducing reliance on manual intervention, APC enhances paper quality consistency, machine runnability, operational stability, and resource efficiency while minimising costs, waste generation, and process variability.
By optimising chemical dosing and maintaining tighter control over key process parameters, mills can improve product consistency, reduce chemical consumption and waste, enhance machine efficiency, increase operational stability, and achieve superior manufacturing performance while ensuring customer satisfaction.
PM: What practical role are AI and machine learning playing in predicting and correcting process deviations before they affect production or quality?
RG: Artificial Intelligence (AI) and Machine Learning (ML) are revolutionising paper manufacturing processes by transforming process data into actionable insights through real-time dashboards, predictive analytics, and intelligent monitoring systems. By integrating data from DCS, QCS, laboratory systems, and online sensors, mills gain enhanced visibility of critical quality and process parameters. Advanced solutions provided by technology suppliers enable early detection of process deviations, help optimise chemical consumption, improve product quality consistency, reduce variability, minimise production losses, enhance machine performance, and support proactive, data-driven decision-making across operations.
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?
RG: Online measurement systems, smart instrumentation, machine vision, and Quality Control Systems (QCS) are enabling paper mills to monitor and control critical quality parameters in real time. Continuous tracking of basis weight, moisture, caliper, ash content, brightness, and formation helps reduce process variability and improve product consistency. Machine vision systems enhance defect detection, while advanced dashboards provide greater operational visibility and faster decision-making. Although challenges remain in measuring certain raw material and wet-end chemistry variations, these technologies significantly improve process stability, machine runnability, quality control, and overall manufacturing performance.
Advanced Process Control (APC) addresses these challenges by leveraging real-time process data, predictive analytics, and multivariable control techniques to optimise chemical dosing and critical quality parameters.
PM: Drives and motion control directly influence speed, tension, synchronization, and machine stability. How are newer drive and control technologies helping mills reduce variability across the paper machine and associated processes?
RG: Drives and motion control systems are essential for ensuring paper machine stability through precise control of speed, tension, synchronisation, and load distribution. Modern digital drives and intelligent controllers continuously optimise machine performance, minimising process disturbances and quality variations. Effective synchronisation across machine sections reduces sheet breaks and production losses, while real-time diagnostics, predictive maintenance, and integrated dashboards enhance operational visibility and decision-making. As a result, mills achieve improved product quality, higher machine efficiency, reduced downtime, and more reliable and consistent manufacturing performance.
PM: How are automation, advanced control, and integrated process data helping mills achieve more consistent paper quality? Which quality parameters have benefited most?
RG: Automation, Advanced Process Control (APC), and integrated process data enable paper mills to achieve superior quality consistency through real-time monitoring, predictive control, and data-driven decision-making. By integrating information from online analysers, QCS, laboratory systems, and process controls, mills can optimise chemical dosing and maintain tighter control over critical quality parameters. Automated adjustments reduce process variability, improve machine runnability, and minimise chemical overconsumption. As a result, mills achieve improved brightness, ash content, moisture, basis weight, and strength properties while reducing waste, lowering costs, and enhancing customer satisfaction.
PM: As control systems become more intelligent, how is the operator’s role in process control and decision-making changing?
RG: As automation, Advanced Process Control (APC), and AI-powered systems become more intelligent, the operator’s role is evolving from manual process control to proactive monitoring and data-driven decision-making.
Operators now focus on tracking trends in critical quality parameters such as basis weight, moisture, caliper, ash content, brightness, pH, retention, and formation through real-time dashboards. Proactive monitoring of chemical consumption, stock consistency, machine efficiency, and process stability enables early detection of deviations and preventive action before quality or production is affected. This approach improves product consistency, reduces variability, enhances machine runnability, and drives continuous operational excellence.
Also Read: Paper Mart Emagazine Aug-Sep, 2026
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.
RG: While advanced automation, AI, and intelligent control systems offer significant benefits, several challenges can limit their effectiveness in paper mills. Legacy automation systems often lack connectivity with modern digital platforms, restricting real-time data utilisation and advanced analytics. In addition, unreliable instrumentation, sensor calibration issues, and data quality gaps can affect control accuracy and process optimisation. Many mills also struggle with integrating DCS, QCS, maintenance, laboratory, and production systems into a single operational view. Furthermore, operators must develop competencies in analysing trends related to basis weight, moisture, brightness, ash content, pH, retention, and chemical consumption. Addressing these challenges through reliable instrumentation, integrated data platforms, and workforce upskilling enables mills to achieve the full benefits of advanced automation, improve process stability, and enhance overall operational performance.
Proactive monitoring of chemical consumption, stock consistency, machine efficiency, and process stability enables early detection of deviations and preventive action before quality or production is affected. This approach improves product consistency, reduces variability, enhances machine runnability, and drives continuous operational excellence.
