How Automation Improved Plant Performance

AKXA Tech Pvt. Ltd. is a technology-driven industrial analytics company built by technocrats with practical field experience in process plant equipment. The company is associated with IIT Madras and is recognized as an IITM incubated company and a Startup India approved company. Its core focus is to develop efficiency-enhancing tools and services for process plants by turning historical DCS data into a usable knowledge resource for better decision-making.

What the Business Needed

The engagement was centered on a simple but critical need: make plant operations more visible, more structured, and less dependent on manual review. In industrial environments, data is usually available in volume, but not in a form that helps operators act quickly.

AKXA Tech’s automation approach was built to close that gap by organizing process data, detecting hidden instability, identifying the real source of variation, and converting that information into practical operating guidance.

This was not about adding another layer of software. It was about making the existing plant data work harder and work faster. Its published solution stack reflects exactly that direction, with data extraction and analytics, PID performance optimization, plant-wide oscillation monitoring, and valve/damper health assessment forming the backbone of the system.

What Was Slowing Operations Down

The main problem was fragmentation. Critical operational signals were spread across multiple control loops, equipment types, and manual checks, which meant that the root cause of a fluctuation was often confused with its symptom. A bin level variation could look like a control issue while the actual fault sat in a discharge gate.

A sudden peak in kiln feed could appear to be process instability, but the real cause could be sensor non-linearity at higher flow. In other areas, loops were being run in manual mode because the team did not trust the automatic response, and that alone created avoidable delays, oscillations, and inconsistent output.

It’s own published case studies show these exact patterns across cement, power, grinding, and other plant environments: discharge gate malfunction, sensor anomalies, suboptimal PID settings, manual mode operation, and external disturbances in draft and drum level control.

The operational cost of this was high. Engineers were spending time validating symptoms instead of fixing the source. Operators were reacting to alarms instead of preventing them. In some loops, the system was technically running, but not in a stable enough way to support consistent production.

The result was time-taking, unstable feed, unnecessary alarms, higher controller error, and weaker visibility into what was actually happening inside the process. It’s site explicitly notes that large DCS data inventories can hide the signatures of plant health rather than reveal them, which is exactly the problem this automation effort was designed to address.

Steps We Took

The solution began with structured data extraction from the plant control environment. Historical trends, loop responses, alarms, and set-point behavior were reviewed together so that the problem could be separated into four layers: instrumentation issues, control tuning issues, mechanical problems, and disturbance-driven variation.

Once the data was organized, each loop was benchmarked against its expected steady-state behavior. That made it possible to identify whether the issue came from a bad sensor response, a valve or gate that was not operating correctly, an aggressive controller setting, or a process disturbance that was not being filtered properly.

From there, the automation workflow was applied in a very practical sequence. Where a loop was stuck in manual mode, it was moved to AUTO only after the response curve was corrected. Where the signal contained false spikes, PV filtering was applied. Where gain and integral action were too aggressive, the PID settings were recalibrated.

Where the final control element was the actual source of instability, the hardware path was corrected before any controller adjustment was made. This approach matters because it avoids the common trap of tuning around a mechanical issue.

Its published case studies show this style of diagnosis repeatedly: gate operation optimization for bin level fluctuation, PV filtering for kiln feed spikes, AUTO mode correction for pre-calciner temperature, fine tuning for cooler under-grate pressure, and parameter adjustments in feeder and drum-level loops.

The automation layer also improved the decision sequence itself. Instead of relying on scattered manual observations, the team could review one connected operational picture: what changed, where it changed, how fast it changed, and whether the deviation came from the process or the controller.

That is the kind of technical discipline AKXA Tech’s platform is built around. Its published approach is to systematically and automatically analyze historical DCS data and convert it into decision support for better asset utilization and productivity.

The Results

• Reduced bin level fluctuations by 70%
• Stabilized process control loops in AUTO mode
• Saved approximately 3.6 TPD of coal
• Improved feeder accuracy from ±0.2 TPH to ±0.05 TPH
• Lowered controller error to below 1%
• Reduced false alarms and manual interventions
• Improved process stability and operational visibility
• Increased mill output and production efficiency by 8%
• Faster identification of root-cause operational issues

Bottom Line

This project showed that business automation in a process plant is not just about digital forms or workflow tools. It is about giving the plant a cleaner, faster, and more reliable way to interpret its own operating data.

By combining analytics, control loop diagnosis, disturbance filtering, and practical tuning, AKXA Tech turned scattered process information into a structured decision system. The result was less instability, fewer false signals, stronger control, and measurable operating improvement.