Project acceptance stalls
Performance targets are not clearly evidenced. Discussions around contractual data and system boundaries slow down final acceptance.
CircularPeak | Independent Consultant and Operator Representative
Assess, optimize, and stabilize plant performance.
Data- and AI-assisted.
I support operators of recycling and sorting plants (MRF, LVP, MSW) in project final acceptance, operational stabilization, and plant optimization using clear operator logic, structured data evaluation, and data- and AI-assisted analysis to unlock the full performance potential of NIR-based sorting technology in day-to-day operations.
Independent
Operator-Focused
AI-Assisted
Response usually within 2 business days. Initial call: 30 minutes with a clear scope.
Typical operational starting points
Purity and recovery fluctuate
Product quality is inconsistent. Material losses increase. Root causes are not transparent.
OEE below target
Downtime is elevated. Subsystems are operating at limits, and robust shift-level data is missing.
Outcome focus: what must count in the end
KPI proof
Transparent before/after comparison with test design for throughput, quality, yield, and availability.
Lower losses
Material streams are optimized with intent: more valuable output, lower losses, and less downtime.
Stable daily operation
Standardized routines and practical training support stable performance at a high level.
Services structured as modules
Final acceptance and performance assessment
Acceptance support for operators with robust, contract-relevant documentation.
Open moduleProcess and plant optimization
Optimization in existing operations focused on sorting quality, recovery, and uptime.
Open moduleStabilization of critical phases
Technical support after commissioning, conversion, or in unstable quality phases.
Open moduleShift-team training on site
Hands-on training in sorting logic, material behavior, and key routines for efficient use of NIR-based sorting technology.
Open moduleInterim management
Temporary technical leadership in demanding project and operating phases.
Open moduleData- and AI-assisted analysis
Analysis support with camera, software, and equipment for faster identification of optimization opportunities in sorting processes.
Open moduleMeasurable project track record
10+
Years in process optimization
25+
International projects
300+
Hours of on-site operational support
Largest single projects
45+
Sorting units
15+
Sorting fractions
175+
kt/y cumulative annual throughput
Data- and AI-assisted analysis in practice
Operational value
- Faster root-cause analysis for quality issues and material losses
- Concrete decision support for operational measures
- Objective data basis for operators, project leads, and management
Use of neutral modern data- and AI-assisted systems for material stream analysis.
Data protection and plant data
- Data collection only for agreed project purpose
- Role-based access to analysis and reporting
- Clearly defined datapoints, no unnecessary collection
- GDPR-compliant processing per project agreement
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How a material stream optimization project runs
Consolidate KPI baseline, operating data, material flow, and shift observations.
Define objectives and target corridors together, for example higher PET, PE, and PP yield.
Use modern data- and AI-assisted systems to identify optimization potential in material streams.
Analyze and interpret data, then translate it into concrete measures.
Implement optimization and establish it as standards in parameters, routines, and processes.
Train teams on the real plant: sorting logic, standards, checklists, and shift priorities.
Review KPI regularly and adjust targets where needed until operation is stable.
FAQ for operators and technical managers
When is external operator representation useful?
Especially during final acceptance, after rebuilds, in unstable KPI phases, or when internal capacity for structured root-cause work is limited.
Which KPIs are typically in focus?
Usually throughput, purity, recovery, yield by fraction, availability, and unplanned downtime.
How does project onboarding work?
With a project check covering target picture, available data, plant status, critical symptoms, first hypotheses, and a realistic work plan.
Is the focus short-term or long-term?
Both. Short term means prioritized bottlenecks. Long term means stable standards and robust control logic.
What is the practical role of AI?
AI acts as analysis support for faster pattern and deviation detection in material streams. Operational decisions remain with the operator team.
Do you have practical experience with NIR-based sorting technology?
Yes. I have spent several hundred on-site hours in NIR-based sorting plants, especially in optimization, process analysis, and operational stabilization.
How is cooperation documented?
With clear formats: diagnostic report, test protocols, KPI comparison, action list, responsibilities, and review rhythm.
Are there manufacturer or supplier interests involved?
No. The work is independent and aligned with operator goals, contract reality, and verifiable outcomes.
How quickly can first effects become visible?
Depending on data quality and process state, often within a few weeks through prioritized actions and initial KPI trends.
Clear next steps instead of long lead times
In the initial call, we align on starting point, KPI priorities, available data, target outcomes, and a reliable project path.
Please include plant type, fractions, main issue, and preferred start window.