Plant energy, yield and quality

Current pain

Operators see feed variability, drifting process conditions and delayed quality results across separate control, historian and lab screens. An energy-saving adjustment can create off-spec output or unstable operation.

Business impact

Excess energy, rework and lost saleable output raise unit cost and constrain capacity. An apparent energy gain is not value if it lowers yield, shifts cost elsewhere or breaches a safety or quality limit.

Redesigned Human + AI workflow

1. The plant team selects one line, product grade and operating window. Process engineering defines the approved limits, baseline and stop criteria before a trial.
2. AI combines permitted sensor, feed, production and lab history with source times and quality checks. It flags missing or suspect readings and prepares an explanation of the conditions linked to energy and yield variation.
3. AI suggests a bounded operating adjustment with expected trade-offs and uncertainty. A qualified process engineer checks the evidence; the operator accepts or rejects the recommendation. Existing control systems and safety interlocks retain authority.
4. The operator runs a small, approved trial within the permitted window. The team records the actual setpoints, feed mix, disturbances, product quality and reasons for overrides. Any limit breach or unreliable data stops the trial and returns to the established operating procedure.
5. The team compares trial results with comparable baseline periods or a defined control, normalizing for grade, feed characteristics, throughput and operating conditions. Quality and safety remain pass/fail guardrails.
6. Finance and the plant owner verify net value from metered energy, saleable output and actual costs, including trial and run costs, before considering wider rollout.

Change and adoption

Co-design the trial with operators, process engineering, quality, maintenance and EHS. Train users to inspect evidence and reject a suggestion with a reason. Review overrides and false alarms in the shift routine. Keep setpoint changes, control-system writes and production decisions under authorized human procedures; scale only after operators trust the workflow and guardrails hold.

Systems and data

Historian and DCS/APC tags with calibration and timestamp status; feedstock quality and batch/grade mix; MES production and downtime records; LIMS quality results with lab delay; energy meters and tariffs; maintenance events and approved operating limits. Align time windows and units, identify sensor drift and missing data, and use only authorized read access during diagnosis.

KPIs with clear definitions

• Energy intensity = metered electricity (kWh) or thermal energy (GJ) / tonne of saleable product, reported separately by energy type and grade.
• Saleable yield = accepted product output / qualified feed input for the defined mass-balance boundary.
• First-pass quality = lots meeting specification without rework / tested lots; report off-spec volume as a guardrail.
• Operating-limit compliance = eligible operating time within approved safety and quality limits / eligible operating time; any critical breach triggers a stop.
• Operator adoption = eligible recommendations reviewed with an accept, reject or override reason / eligible recommendations.

Measurable outcomes and verification

Establish baseline variability before setting targets. Compare matched shifts or a pre-agreed control while accounting for grade, feed, throughput, weather and maintenance. Check energy, yield, quality, downtime and safety together. Count only attributable, finance-reviewed net benefit after extra energy, scrap, trial and operating costs; never present an illustrative plant result as a RightSense achievement.