SUMMARY:
Close the gap between how a plant could run and how it actually does. This role exists to reduce variability, improve stability, and hardâwire optimisation into daily operations. Using advanced control, analytics, and systemâenabled decision support, you’ll turn operational insight into repeatable execution — not recommendations that sit on a slide deck.
POSITION INFO:
Enable stable, predictable, and optimised processing operations through the design, deployment, and sustained adoption of digital process control, advanced analytics, and system-enabled optimisation capabilities.
This role is accountable for
how the plant is controlled and enabled rather than metallurgical ownership. It provides deep technical and analytical capability to improve operational stability, controllability, and optimisation through:
- Process-aware digital control and optimisation;
- Advanced Process Control (APC) deployment, tuning, and adoption;
- Applied analytics, performance modelling, and AI readiness;
- Dynamic water balance system enablement; and
- Early detection and response to operational variability and instability.
The position has a strong applied analytics mandate, delivering repeatable, data-driven insights that explain performance variability, quantify control effectiveness, and support sustainable optimisation decisions.
Acting as a technical enabler, the role connects plant operations, control rooms, and digital platforms to translate optimisation intent into repeatable, governed, auditable, and scalable operational execution through IOC routines, APC systems, and analytics-driven decision support.
- Operational Optimisation:
- Provide technical input and optimisation requirements for the definition, review, and maintenance of MES (Level 2–3) data context standards, including tag naming conventions, unit standards, asset hierarchies, and event models, to support advanced control and analytics.
- Design, deploy, and continuously improve loop tuning and advanced process control (APC) programmes, ensuring embedded adoption through control room and IOC routines.
- Develop and maintain analytical models to quantify process stability, variability drivers, control effectiveness, and optimisation benefits.
- Perform diagnostic and statistical analytics, including trending, variance analysis, correlation analysis, and regime detection, to identify root causes of control degradation and performance deviations.
- Interpret high-level plant indicators, such as instability, excursions, and abnormal operating conditions, to inform control strategy enhancements and APC tuning.
- Analyse operational variability driven by feed changes, operating modes, and process conditions, to improve detect-to-correct response through system-based logic and automation.
- Implement and technically support dynamic water balance systems, ensuring live accuracy, visibility, and integration into operational dashboards and compliance reporting.
- Partner with data engineering teams to publish contextualised, analytics-ready, and AI-ready time-series data into enterprise analytics environments.
- Design and maintain automated detection, alerting, and visualisation mechanisms for early identification of instability, abnormal conditions, or optimisation erosion.
- Maintain a high-level understanding of processing circuit interactions (e.g., crushing, milling, classification, and concentration) strictly to support accurate data context, analytics, and control modelling, without assuming metallurgical decision authority.
- Key Analytical Deliverables:
- Control and optimisation performance models, including loop stability indices and APC effectiveness metrics.
- Root cause analysis outputs explaining control- and system-driven performance deviations.
- Optimisation and APC benefit tracking models with defined baselines and measurable performance improvements.
- Predictive or pattern-based indicators supporting early detection of abnormal or unstable operating conditions.
- Analytics-enabled IOC dashboards with clear thresholds, alerts, and decision triggers.
- People Enablement:
- Coach operators, control room personnel, and engineers on APC usage, analytics interpretation, and control optimisation principles.
- Contribute to IOC and control room training materials, including SOPs, runbooks, and optimisation playbooks.
- Support structured operational performance discussions through analytics-driven insights rather than descriptive reporting alone.
- Resource & Delivery Management:
- Prioritise APC, analytics, and process control improvement backlogs in collaboration with optimisation and automation leadership, based on value, risk, and operational readiness.
- Plan and coordinate testing and deployment windows aligned with OT change management, approval processes, and system continuity requirements.
- Support optimisation governance by establishing quantified baselines, benchmarks, and improvement tracking through analytics.
- Stakeholder Engagement:
- Engage with plant operations, engineering, maintenance, control rooms, automation teams, and data specialists to support optimisation enablement.
- Interface with APC, MES, analytics, and automation solution providers to support deployment and continuous improvement.
- Collaborate with operational leadership to ensure that analytics-driven optimisation initiatives align with plant priorities and do not introduce unmanaged risk.
- Risk, Safety & Compliance:
- Ensure that all MES contributions, APC logic, analytics models, and loop tuning changes comply with site safety rules and approved management-of-change processes.
- Ensure that dynamic water balance systems and optimisation analytics are accurate, auditable, and defensible for compliance purposes.
- Ensure that optimisation activities respect operational envelopes, safety constraints, and production priorities.
- Design optimisation systems that enhance early identification of safety-relevant deviations, reducing reliance on reactive or manual intervention.
Role Outcomes & KPIs:- Improved process stability and controllability.
- APC adoption across approximately 70% of targeted circuits.
- Loop tuning adherence of approximately 85%.
- Dynamic water balance compliance of approximately 95%.
- Reduced unexplained process variability.
- Reduced time from deviation detection to corrective action.
- Increased percentage of optimisation initiatives supported by quantified analytics evidence.
- Sustained IOC adoption of analytics-driven decision support.
- Minimum Requirements:
- Grade 12 (NQF 4).
- BEng or BSc in Chemical Engineering, Industrial Engineering, Electrical (Control) Engineering, or equivalent.
- Advantageous Certifications:
- Lean or Six Sigma certification.
- APC, MES, control systems, or industrial analytics certifications.
- Experience:
- Minimum of 5 years’ experience in process control, advanced automation, or analytics-driven optimisation within mineral processing or heavy industrial environments.
- Hands-on experience in loop tuning, APC deployment, and control optimisation.
- Proven ability to apply analytics to explain operational variability and quantify optimisation impact beyond descriptive reporting.
If you are interested in this opportunity, please apply directly.
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