Process audit of chemical plants for sustainability and optimized operations: MP Sukumaran Nair, Director, Centre for Green Technology & Management

New developments are primarily focused on integrating Industry 4.0 technologies—such as AI, IoT, and real-time analytics—directly into the auditing workflow to improve accuracy and operational efficiency

Chemical plants are complex networks of systems with varying thermodynamic characteristics, interacting continuously to achieve the designer's intent. Distillation columns, reactors, furnaces, fired heaters, rotating equipment, pipelines, heat exchangers, other utilities and instrumentation must all work in precise harmony so that the production process goes smooth. These  plants operate in a highly competitive environment where profitability, operational and reliability are inseparable. These elements are deeply intertwined with sustainability, as efficient processes minimize energy waste, reduce emissions, and extend asset life. Profit margins in the chemical industry depend heavily on optimized operations, equipment reliability, and the onstream factor—all of which directly influence energy consumption, raw material efficiency, environmental performance, and plant safety. In essence, sustainability is not an external add-on but an intrinsic component of efficient operations.

This complexity makes regular monitoring and periodic process audits indispensable. A process audit evaluates performance, safety, energy usage, and environmental impact. It identifies shifting operating lines, detects abnormalities, flags unsafe situations, and exposes inefficiencies. The outcome is a structured set of corrective actions that reduce wastage of energy and materials, uncover opportunities for retrofits and innovation, and enhance overall plant sustainability.

Why audits matter

Audits reveal shifts from optimal operating lines, exposing abnormal conditions and flagging emerging unsafe situations. They trigger corrective actions that unearth opportunities to cut energy and material wastage, while paving the way for targeted retrofits and innovations. In olefins plants, for instance, minor tuning gaps in high-leverage units can cost millions annually in lost margins, compounded by inefficient firing rates and compressor loads—issues audits address head-on.

Key audit benefits include improved onstream time by identifying capacity reductions, efficiency losses, and breakdowns, reduced  energy consumption by way of adiabatic  heat recovery, preventing  overheating, reducing material and  heat loss from leakage and poor insulation and  sustainability gains through improved process efficiency, lower CO₂ footprints via optimized fuel use, burner management and emissions control. 

Conducting a comprehensive process audit

A structured audit starts with calibration of instruments for  data collection, review of  daily log sheets  and listing key process parameters. The next step is to conduct test runs of the process and also on the plant equipment, analyze feedstock quality via laboratory testing, and scrutinize areas of concern across the facility. The Technical service engineer conducting the audit shall prepare a plant specific checklist/questionnaire before starting the audit function.

1. Calibration and verification of instruments : Reliable data begins with accurate instruments. Programmable Logic Controller (PLC), Distributed Control systems (DCS)  and Fieldbus-based systems must be checked for drift, loop accuracy, alarm performance, and communication reliability.

2. Data collection and daily log review: Historical and real-time data—temperatures, pressures, flows, compositions—provide the baseline. Daily log sheets often reveal slow drifts or early indicators of failure.

3. Listing and evaluating key process parameters: All critical parameters are identified: reactor conversion, column reflux ratios, furnace excess air, compressor load, pump performance, steam usage, cooling water flow, etc.

4. Test runs of plant and critical equipment: Controlled test runs validate performance against design intentions and nameplate capacity.

5. Feedstock quality and chemical analysis: Feed variability directly affects product quality and energy demand. Periodic cross-checks of laboratory  and on the online analyzer results are essential.

Areas of concern in process audits

The audit shall cover all Process Units and Unit Operations within the facility.

The future of process audits

Modern chemical plants are moving beyond periodic audits to continuous AI-driven monitoring systems. A number of modern tools and techniques are available to empower the audit engineer, a brief review of some of  which is given below:

1. Advanced Rigorous Plant Monitoring

Here, model-based techniques and high-frequency data capture enable deeper process diagnostics. These monitoring technologies  integrate sensors, automation systems, and advanced analytics and impart real-time visibility into critical parameters.   ARPM has  emerged as the backbone of safer, more reliable, and more efficient operation of chemical plants.

2. AI-Powered Continuous Auditing

This is a proactive, technology-driven approach that combines  artificial intelligence (AI), machine learning (ML), and real-time data analysis into daily plant operations to monitor output, quality, safety and regulatory compliance. Unlike traditional, periodic audits that rely on retrospective, sample-based reviews, this method monitors the entire  production process  in real time. Here algorithms detect anomalies, optimize setpoints, and recommend actions in real time.
3. Real-Time Data Analytics

It involves instant  analysis of  data from sensors, IoT devices, and production systems as it is generated, allowing for immediate process adjustments, predictive maintenance, and quality control. Here, high-resolution process data supports predictive insights and enable faster decision-making.
4. Predictive Maintenance

A better-known tool to process engineers,  PM  proactively analyze machinery  vibrations and other  process parameters  and predict  failures well before they occur thereby  reducing downtime, enhancing safety, and optimizing maintenance costs of process equipment and systems in real-time. Machine-learning models forecast equipment failures before they occur.
5. Reliability Engineering Tools

These methodologies, software, and analytical techniques are used to ensure equipment and processes perform their intended functions without failure, maximizing uptime, safety, and efficiency. They  are essential for managing high-risk operations, minimizing costly unscheduled downtime, and optimizing maintenance strategies. It includes Root Cause Analysis (RCA), Reliability Centered Maintenance (RCM), Failure Mode and Effects Analysis (FMEA), Weibull analysis – all improve plant longevity and safety.

6. Energy Use Monitoring

This is a systematic, real-time tracking and analysis of energy consumption -electricity, gas, steam, other forms of fuel- across production assets to improve efficiency, reduce carbon emissions, and lower operational costs. Its online dashboards track real-time energy intensity and emissions.

7. IIoT and Smart Sensors

It drives efficiency and safety by enabling real-time monitoring of process parameters such as pressure, temperature and liquid levels. It signals  predictive maintenance to prevent unplanned downtime, optimizing process conditions and energy consumption and  ensure leak detection. Enabled wireless sensors also monitor vibration, corrosion, temperature, and fugitive emissions.
8. Digital Twins

These virtual replicas of physical assets or processes, enable real-time simulation, monitoring, and optimization. They enhance performance through predictive maintenance, process optimization, operator training, and improved safety, reducing downtime, optimizing energy efficiency  and identifying  optimum operating windows.

9. Edge AI Monitoring

Here,  sensors process data  at the equipment level without latency and hence  quickens  response to act. This helps to optimize processes by managing reaction conditions (temperature, pressure), enhance predictive maintenance of equipment, identify process hazards early, enable autonomous quality control, reducing material waste and thus improve manufacturing efficiency.
10. ESG and Sustainability Audits

There are a set of tools to comprehensively and independently evaluate a company's environmental, social, and governance (ESG) practices. These audits are used to verify compliance with  environmental regulations, identify operational lapses, manage risks associated with hazardous substances, and validate sustainability claims to investors and customers to avoid criticism and  litigations. Usually, they  are integrated into periodic audits to support compliance, investor expectations, and long-term sustainability.

Conclusions

Audits are not just compliance; they are profit engines for chemical plants facing tightening of margins. The landscape of process plant monitoring and audits is undergoing a significant shift from traditional, periodic snapshot assessments to continuous, data-driven oversight. New developments are primarily focused on integrating Industry 4.0 technologies—such as AI, IoT, and real-time analytics—directly into the auditing workflow to improve accuracy and operational efficiency. Due to rising costs and regulations, audits now heavily focus on machine-level energy tracking and emissions. As plants become more connected through IIoT, assessing digital controls and data security has become a critical part of the plant monitoring audit process. Auditors are increasingly tasked with verifying Environmental, Social, and Governance (ESG) disclosures to meet stakeholder demands for transparency. By combining sound engineering practices with modern digital tools, chemical plants can achieve higher profitability, reduced carbon footprint, improved reliability, and sustained competitive advantage.

(Mr. Nair is a Former Secretary to Chief Minister and Chairperson, Public Sector Restructuring & Audit Board, Government of Kerala.)

Disclaimer: These are the personal opinions of the author.