Industrial operations account for a large share of global energy use and CO₂ emissions. For a process plant, the energy that drives those emissions is also one of its largest controllable costs. Lower energy per tonne of product is a carbon result and a margin result at the same time, which is why the energy transition is as much an operating question as a capital one.
Much of the opportunity sits in how existing units run today, in refineries, chemical plants, cement plants and mineral processing plants. A large part of the data a plant already collects is never used in process analysis. A learned process model can turn that history into operating decisions, as covered in manufacturing data analytics.
Where linear APC reaches its limits
Advanced process control (APC) has delivered value for decades. Most APC is built on linear models identified around a narrow operating region. Those models need re-identification and retuning as feeds change, equipment fouls and the unit moves to a different operating point.
Real units are nonlinear. Temperature, pressure, flow and composition interact, and the relationships between them change across the operating range. A linear model holds well near the point it was identified on. Away from that point the controller either runs conservatively or stops capturing value. In energy terms, the cost shows up as extra firing, extra steam, extra recycle and wide margins held against constraints.
A learned approach works differently. A deep learning model learns the unit's nonlinear dynamic behavior from its own operating history, guided by first principles. A controller trained by reinforcement learning across many simulations learns how to move setpoints within the unit's constraints. It runs above the existing APC and DCS, writes setpoints through the infrastructure operators already use, and operators can take over at any time.
Two concerns usually stop adoption: the model is seen as a black box, and integration looks complex. Each recommendation can show which process variables drove it, and the controller writes its moves through the existing APC and DCS, so neither concern needs to stall a project.
A four-phase plan for energy-intensive units
The plan starts on one unit and extends to others once that unit has shown its value.
Phase 1: assess and prioritize
- Audit the data. Check historian data quality: clean, consistent tags that cover the range of conditions the unit actually runs, including feed changes and seasonal swings. Confirm DCS connectivity and that the control system can accept external setpoint changes. Fixing data issues before modeling avoids delays later.
- Map carbon against margin. Build a heat map of units by energy use, emissions and margin effect. Rotary kilns, distillation columns and polymer finishing lines usually rank high.
- Set baselines. Document current energy consumption, throughput and emissions. The baseline is the reference that proves value to skeptical stakeholders.
- Align the teams. Operations, engineering and management need the same targets. A champion engineer who understands both the technical constraints and the business objectives keeps the work grounded.
Phase 2: prove value on one unit
Build a model for one high-energy unit with energy reduction as the target. Define the KPIs and the measurement protocol before training begins: energy consumption, product quality and operating stability.
Train the model on historical data and involve operators from the start. Workforce acceptance is a real constraint, and early operator involvement brings out practical knowledge that data analysis alone can miss. Complex units need iterative refinement, so set expectations accordingly.
Phase 3: close the loop
Connect the controller to the DCS so it moves from advisory recommendations to writing setpoints within limits that operators set. Operators stay on the loop, watching from the board.
Explainability matters most at this step. Show operators why each move was made, and build feedback loops so their process knowledge improves the model. Train crews on the new workflow with operating safety as the overriding constraint, and position the controller as a tool that works with their judgment.
Phase 4: scale and sustain
Put KPI governance in place that tracks technical performance and business outcomes together: energy savings, emissions and margin, along with model reliability across operating conditions. Plan for retraining as feeds, fuels and product requirements change. Extend to further units using what the initial unit taught the team.
Measuring what matters
Three groups of measures tell the story: carbon, financial return and operator confidence.
Carbon
For fuel-related emissions, a working estimate is:
annual CO₂ avoided (t/yr) = baseline energy use (MMBtu/day) × emission factor (tCO₂/MMBtu) × fractional energy reduction × operating days per year
Compare at the same production rate, or normalize to energy per tonne of product, so a rate cut does not read as an efficiency gain. In cement, fuel savings reduce combustion CO₂, while the CO₂ released by calcining limestone follows clinker output, so track the two separately.
Financial return
Total annual benefit = energy savings + yield improvement + reduced off-spec and non-prime production, each in $/yr. The investment case should net out implementation and ongoing support costs.
Operator confidence
Track the recommendation adoption rate, the share of recommendations operators accept and act on. A rising rate indicates both model accuracy and operator trust. Monitor recommendation frequency and accuracy over time so reliability holds as process conditions change.
Executive dashboards work best with rolling 30-day averages for each measure and variance bands that show the normal operating range against optimized performance.
Pitfalls that stall projects
Technical
- Historian data quality. Poor historian data is a common roadblock. Many plants find gaps, inconsistent formats or measurement errors in sensor data. Clean it before deployment.
- Black-box perception. Operators resist recommendations they cannot understand or verify. Daily explainability reports that show which variables drove each recommendation help. When the team sees the model has found a temperature and pressure relationship they had not considered, confidence builds.
- Skills gaps. Control engineers need new skills, and data scientists need process knowledge. Cross-training the existing team is usually more effective than hiring new talent, because control engineers already know how the plant behaves.
Organizational
- Scope creep. Lock the KPIs and baselines up front: how much energy reduction counts as success, measured over what period, on which units. Without that clarity, expectations drift and value is hard to demonstrate.
- Misaligned incentives. If operations bonuses depend on holding production rates, teams will resist moves that trade a little throughput for efficiency. Tie part of performance to energy KPIs.
- Change management. Be clear that the system supports operator expertise. Involve operators in model validation from the start. When they help verify recommendations against their own knowledge, they become advocates.
Regulation and market shifts
Carbon pricing schemes are expanding, and border carbon adjustments put a cost on the emissions embedded in imports. The U.S. Energy Information Administration expects global energy consumption to keep rising through 2050, so energy saved at the unit level is likely to stay valuable.
Fuels and feeds will also change. Bio-feedstock blending and hydrogen-enriched natural gas both shift how a unit behaves, so the models behind optimization need to be retrained as they do. Plants with several similar units also gain when what is learned on one unit can be applied to the others.
How the Imubit Platform applies
Imubit applies Map, Model and Control to energy-intensive units across refining, chemicals and polymers, cement, and mining and minerals. Map traces excess energy and emissions to the recurring decisions behind them, such as firing, steam and recycle targets. Model uses Deep Learning Process Control® (DLPC®) to learn each unit's nonlinear behavior from its own history, guided by first principles. Control takes those decisions to closed loop, writing setpoints to the existing APC and DCS, inside limits set by plant engineers. Where energy is the optimization target, Imubit reports a 15 to 30% reduction in energy consumption. More on the approach is on the platform page.
