How Bravura AI helped a field engineer walk into unfamiliar code, solve a freeze-valve
crisis, and hand off complete documentation—all before the weekend was over.
| Hours (not days) Time-to-Productivity | 3 Days (vs. 6–7 typical) Project Duration | Repeat Business Won Outcome |
The Challenge
A Western Midstream natural gas facility in Casper, Wyoming was facing an urgent operational threat as winter approached. Their swing bed dryer system—a three-bed arrangement where two beds dry gas while the third regenerates—had developed a dangerous flaw in its control sequence.
When the regeneration cycle drove humidity out of the dryer beds, that moisture was condensing against cold exposed pipes and freezing solid inside the valves. The result: immobilized valves, a dryer forced offline, and operators out in sub-zero Wyoming weather manually applying heat to melt the ice before operations could resume. With temperatures set to drop further, the window to fix the problem was closing fast.
Brian McWhorter, Bravura-AI founder and CEO, also a senior automation engineer, was asked to go on-site and implement a solution. The constraints were significant:
- He had never seen the facility’s DeltaV automation code before
- He had no remote access to the system prior to arrival
- The regeneration cycle ran every 18 hours—he would have at most two live test windows
- The total engagement was capped at four days
The Approach
Step 1 — Rapid Ramp-Up With Bravura AI
Normally, walking into an unfamiliar control system means spending one to two days just reading through the development environment—clicking between screens, cross-referencing documentation, and piecing together how the automation was implemented before any actual engineering work can begin. On a four-day engagement, that burn rate is project-threatening.
With Bravura AI, Brian uploaded the entire codebase and had the platform generate a line-by-line narrative tied directly to specific references in the code. Within a couple of hours—not days—he had a comprehensive picture of exactly how the DeltaV sequence operated, including the valve sequencing, compressor start logic, and temperature measurement points that governed the drying and regeneration cycles.
This also eliminated a common field hazard: operator memory. Site personnel often describe system behavior from habit or assumption, which can contradict what the code actually does. Having an accurate, AI-generated narrative of the code meant Brian could verify every statement against a ground-truth reference, not word-of-mouth.
Step 2 — Precise Code Review and Change Planning
With a working understanding of the system in hand, Brian sat down with the site engineers and the P&ID diagrams to pinpoint the specific valve exposed to wind and cold, and to plan the modification needed to eliminate the freeze events.
The root cause was a regeneration ramp rate that drove off too much moisture at once, producing enough humidity to condense and freeze at the vulnerable valve. The solution required adding a carefully designed intermediate step: a slower initial ramp to a mid-range temperature that would drive off moisture gradually, followed by a final ramp to full drying temperature once the majority of the water had been removed.
Because Bravura AI presented the entire sequence as a readable narrative in one place—rather than requiring Brian to navigate through DeltaV’s fragmented development interface—he could pinpoint exactly where the new steps needed to be inserted, identify the correct module blocks for the affected valves, and confirm his understanding with site staff before writing a single line of new code.
Step 3 — Implementation and Live Testing
By the end of Day 1, Brian had written the modified code. Given the 18-hour regeneration cycle, that was the earliest the first live test could run. The initial test was a success—the sequence ran completely through without faulting—but the operators needed a user-facing variable added to the HMI so they could set and monitor the timing for the new intermediate step. That was created and integrated.
Eighteen hours later, early on Day 3, Brian was on-site for the second run. Tuning parameters were adjusted, the regeneration sequence ran from start to finish without issue, and the site team called it a success.
Documentation in Hours, Not Days
Completing the technical fix is only half the job. Proper handover documentation—capturing what was changed, why it was changed, new alarm settings, updated HMI graphics, and operating instructions for the crews—typically consumes an additional one to two days of an engineer’s time. Skipping it is not an option: without it, the next technician is flying blind, and operators have no reference for how the modified system is supposed to behave.
With Bravura AI’s built-in documentation capabilities and LLM integration, Brian was able to generate that deliverable package on Day 3—the same day the final test ran:
- A line-by-line narrative of the code changes, with clear explanations of each modification
- Justification for why each change was made, tied to the freeze-prevention objective
- Updated alarm settings and new tuning parameters
- Screenshots of the new HMI faceplates and graphics operators would interact with
- A four-to-five page operator manual covering everything the shift crews needed to know
- A separate technical reference for maintenance technicians, with direct code citations
Had this situation occurred now, this client would opt for continued access to PlantUnity and its wide array of benefits and deep insights. The engineers who had witnessed the ease of high-quality documentation creation especially vouched that it would pay for itself many times over.
Results
| Metric | Outcome |
|---|---|
| Time to understand unfamiliar code | ~2 hours with Bravura AI (vs. 1–2 days typical) |
| Total project duration | 3 days (vs. 6–7 days estimated without Bravura AI) |
| Live test cycles used | 2 of 2 available — both successful |
| Documentation turnaround | Same day as final test, included in project timeline |
| Customer satisfaction | Repeat engagement requested before engineer left site |
| Follow-on business | Cause-and-effect analysis project awarded |
“We were impressed by how quickly the engineer understood our system and by the quality of the documentation he left behind. We wanted him to come back for a larger project before he even made it home.”
— Operations Representative, Western Midstream
Why It Matters
This engagement illustrates what Bravura AI enables at the field level. The bottlenecks that pad automation projects—ramp-up time, cross-referencing disparate documentation, writing handover reports from scratch—are not eliminated by hiring more experienced engineers. They are structural inefficiencies in how industrial control code is read and documented.
Bravura AI removes those bottlenecks by making the code readable and the documentation automatic. The result, in this case, was a skilled engineer operating at full effectiveness within hours of arrival, completing a complex multi-cycle test-and-tune engagement in three days, and handing off a professional documentation package that would have taken another day or two to produce by conventional means.
For asset owners and operators, that efficiency translates directly into lower cost per engagement, faster return to normal operations, and a clearer record of every change made to their systems—maintained and accessible for whoever comes next.
About Bravura AI — Bravura AI (Plant Unity) is an AI-powered platform purpose-built for industrial automation engineers. It translates control system code into human-readable narratives, accelerates onboarding on unfamiliar projects, and generates professional change-management documentation—turning days of ramp-up into hours and keeping every modification permanently on record.