Xpdeep
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INDUSTRIAL & PROCESS

Recover the process margin
that pays for uncertainty.

Plants hold material, energy and quality margins because process state and product quality are not known precisely enough. Xpdeep makes process models readable from within and their domain explicit, so engineers can see what drives an outcome and examine which margins compensate for uncertainty. Where useful, a separate constrained intervention can be proposed; the plant’s control system retains authority.

MATERIAL · ENERGY · YIELD · QUALITY · THROUGHPUT · UPTIME

THE SURROUNDING CONTROL SYSTEM REMAINS IN CONTROL

01 PROCESS STATE

Process-state and quality uncertainty: a model should show what is driving the outcome now.

On temporal data, Xpdeep exposes not only which variables matter, but which segments and signal morphology carry the model output.

ILLUSTRATIVE PROCESS VARIABLES

  1. MULTIVARIATE PROCESS INPUTS
  2. STRUCTURAL READING
  3. OUTCOME

Structural explanation and signal-level localization help engineers see where the learned decision is actually being made.

02 MODEL ENGINEERING

Training should stop being blind trial and error.

  1. INITIAL MODEL
  2. READ LEARNED STRUCTURE
  3. DIAGNOSE PROBLEM REGION
  4. TARGETED LOCAL EDIT (NATIVE)
  5. VERIFY EFFECT

REST OF MODEL FROZEN · DESIGN-TIME EDITING REQUIRES A NATIVE XPDEEP MODEL

Xpdeep can reduce unnecessary inputs, align the model to the true business KPI and adjust complexity only where performance losses concentrate.

105 → 19

INPUT VARIABLES

UP TO 82%

FEWER INPUT VARIABLES AT EQUAL OR BETTER ACCURACY

UP TO 4×

FASTER MODEL DEVELOPMENT

Independent programme evaluation.

03 CONSTRAINED INTERVENTION · WHERE USEFUL

Where useful, a separate inference
proposes what can change now.

On supported time-series and tabular models, Xpdeep can compute a minimal feasible input change under specified operational constraints, with the structural justification of why it changes the predicted outcome. The proposal is an input to the plant’s control system, which decides whether to act, hold or escalate.

IMPORTANT BOUNDARY

Structural, not automatically causal.

Minimal means minimal feasible under the stated constraints, and optimization procedure, not a unique global optimum.

CONSTRAINTS

A feasible intervention must respect the process you actually operate.

ILLUSTRATIVE · 1: CONSTRAINTS THAT DEFINE A FEASIBLE INTERVENTION

1 · FEASIBLE INTERVENTION

Operating limits, actuator ranges and process constraints define which interventions are feasible. The diagram shows this decision space.

2 · PHYSICS IN LEARNING

Separately, physical models and constraints can be integrated into learning. This has been done on a continuous-process project.

CONTINUITY

A constrained adjustment may help avoid an unnecessary stop, once the plant’s system authorizes it.

TODAY

  1. PREDICTED PROBLEM
  2. ALERT
  3. STOP / HUMAN REVIEW

WITH A CONSTRAINED INTERVENTION

  1. PROCESS STATE + DOMAIN
  2. CONSTRAINED INTERVENTION
  3. SYSTEM AUTHORIZATION
  4. CONTINUE

EXCEPTION

  1. HOLD / ESCALATE
  2. HUMAN

POTENTIAL OPERATIONAL CONSEQUENCES

  • Fewer unnecessary stops
  • Faster response
  • More continuous operation
  • Fewer unnecessary reviews
  • Expert attention focused on exceptions

The surrounding control system remains in control. A computed intervention is an input to it, never an authorization to continue.

04 MATERIAL · ENERGY · QUALITY · YIELD · THROUGHPUT

The value is not another prediction.
It is a better operating decision.

ECONOMIC SURFACES TO MEASURE

  • MATERIAL

    Evaluate material consumed to compensate for uncertainty about process state or distance to a quality limit.

  • ENERGY

    Evaluate energy per unit produced through constrained operating adjustments.

  • YIELD

    Less value lost to avoidable process decisions

  • THROUGHPUT

    Fewer unnecessary interruptions

  • QUALITY

    Earlier, better-attributed decisions

  • UPTIME

    More continuous operation where conditions permit

Material and energy economics concern the customer’s process. Model frugality concerns compute and memory on the deployment target. These are opportunities to establish against agreed KPIs; no measured customer energy or material savings are claimed.

ONE TRANSFORMATION PATTERN

Recover performance currently left unused because the system does not know enough about its own state.

PROCESS OPERATING ENVELOPE

  1. STATIC PROCESS LIMIT
  2. CURRENT PROCESS STATE + HISTORY + DOMAIN STATE
  3. CONDITIONAL OPERATING REGION
  4. ACT / HOLD / ESCALATE
  5. BASELINE PRESERVED

POTENTIAL SURFACES

  • Throughput
  • Energy efficiency
  • Quality
  • Production rate
  • Availability

Illustrative transformation pattern. Xpdeep does not remove physical or customer-defined hard limits.

DATA · VALIDATION · EVIDENCE

What the plant measures, and where the evidence stands.

RELEVANT DATA AND MODEL TYPES

  • Process time series and setpoints
  • Quality and laboratory results (tabular)
  • Equipment condition signals
  • Camera-based inspection models where quality is checked visually

WHAT THE CUSTOMER MEASURES OR VALIDATES

  • Model accuracy and domain coverage on its own process data
  • Any setting change, within approved limits and against agreed KPIs
  • Physical and engineering validation where the process requires it
  • Integration with APC, MPC, DCS and MES

PUBLISHABLE EVIDENCE AND MATURITY

INDUSTRIAL DATA
Core Xpdeep model-level capabilities are available and have been applied to industrial process data. Physical models and constraints have also been integrated into learning in continuous-process work.
MODEL PERFORMANCE
On time-series data, Xpdeep models achieve accuracy at least equivalent to standard deep learning and frequently superior. Independent programme evaluation: 105 → 19 input variables; up to 4× faster model development.
SIMULATED FEASIBILITY
XpProcess Phase 1 demonstrated a TRL 4 prototype in a physically constrained simulated CSTR environment. Prediction and structural explanation were evaluated on simulated data; proposed setpoint changes were checked against simulator constraints.
STATUS
XpProcess remains In Development. No validated real-plant performance, autonomous plant operation or measured customer material or energy saving is claimed.

INTEGRATION

Xpdeep changes the model.
Not your plant stack.

  1. SENSORS / ACQUISITION
  2. DIGITIZED PROCESS DATA
  3. XPDEEP MODEL
  4. APC / DCS / MES / CONTROLLER / APPLICATION

APC, MPC, DCS, MES and existing process-control systems remain in place. APC and MPC already perform predictive optimization under constraints; Xpdeep does not replace them. Its distinction is at the model level: structural evidence, attributed domain awareness, targeted structural engineering and, where supported, constrained interventions with structural justification.

NO PROPRIETARY TRANSPORT OR LOGGING FRAMEWORK IMPOSED.

Industrial engineering boundaries.

EXPAND

  • APC, MPC, DCS and MES remain in place.
  • Structural integration requires read access to the PyTorch architecture and weights, or a Native model.
  • Operating limits stay with the customer.
  • Production integration and target profiling remain customer-specific.
  • The customer’s surrounding system defines and authorizes actions.
  • Attributed domain state covers the retained model inputs; alone, it does not authorize action or establish system safety.
  • Structural justification does not automatically establish physical causality.
  • Minimal means minimal feasible under the stated constraints and optimization procedure.
  • Targeted editing and controlled correction require Native mode.
  • System validation and certification remain with the customer and relevant authority.
  • Reduced requalification scope requires application-specific evidence and acceptance.

START WITH ONE FUNDED PROCESS PROBLEM

Start with a KPI the plant already measures.

  • PROCESS PREDICTION

    A process or quality prediction engineering cannot yet explain

  • QUALITY

    A decision tied to defects, off-spec product or laboratory results

  • MATERIAL

    Material consumed to compensate for uncertainty about process state or a quality limit

  • ENERGY

    Energy per unit tied to conservative operating settings

  • DRIFT DIAGNOSIS

    A model or process whose behaviour changes and needs a diagnosed cause

  • CONSTRAINED ADJUSTMENT

    A setpoint decision where a constrained, justified proposal could help

A first engagement does not have to include an autonomous intervention.

Phase 1 is a funded engagement, typically three to six months, on a consequential model, decision, function or programme you have prioritized. Agree the economic, operational, quality, mission, product or assurance success criterion at scoping; work on your data within your engineering environment and constraints.

Xpdeep supplies model-level technology and repeatable software deployment, not a replacement engineering lifecycle. Your teams and implementation partners can integrate and validate the model using your own tools and test processes.

  1. 01 →

    One funded problem

    A model, engineering problem, product function, subsystem or decision the business already owns.

  2. 02 →

    Agreed criterion

    Data, duration, success criterion and validation method defined at scoping.

  3. 03 →

    Evidence on your application

    Results assessed through the customer’s validation process; a side-by-side model comparison is one possible method.

  4. 04

    Define the extension

    Identify the possible Phase 2 scope at the start; use Phase 1 evidence to determine its scope.

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