When Generic Asset Management Meets Lithium Mining

1.0 Introduction.

Lithium has become a cornerstone mineral for batteries and energy storage however, its extraction environments sit at the edge of what many generic asset management implementations were originally designed to handle.

Leaders across maintenance, operations and technology often ask the same difficult questions such as:

1.     Why do generic maintenance/asset management strategies fail sometimes in lithium environments?

2.     Why do standard PM templates misjudge degradation?

3.     Why do data structures that work in other industries fall a bit short under lithium’s chemistry, geology and ESG pressures?

This article does its best to shed some light on these questions by examining the physical, operational and regulatory realities that shape lithium extraction. 

Brine operations and hard rock spodumene mines stress equipment through combinations of chemical attack, scaling, abrasive wear and thermal cycling that are not reflected in industry neutral asset management templates.

As a result, the challenge is not that the commodity itself changes the CMMS/EAM/ERP software, but that the extraction context invalidates the assumptions baked into generic asset management strategies that would lie within. Brine systems handle high salinity fluids with complex mixtures of sodium, calcium, magnesium and other ions, which drive aggressive scaling and corrosion in pumps, pipelines and evaporation equipment.

Hard rock circuits process ores with moderate to high hardness and significant abrasiveness, creating demanding conditions for crushers, mills and classification equipment.

These environments tend to push maintenance teams to prioritise different inspection triggers, condition indicators and spare part holding policies than those found in standard PM setups.

At the same time, lithium projects often sit in remote basins, deserts or high altitude regions with limited grid power, constrained logistics and patchy telecommunications.

ESG expectations and water related scrutiny are also higher than in many conventional commodities because of the perceived footprint of both brine evaporation and emerging direct lithium extraction technologies.

The result is a context where maintenance strategies should be deliberately tuned to local realities rather than treated as generic, drop in data.

2.0 Why Generic Maintenance Strategies Might Miss The Mark.

Generic maintenance strategies are usually built on three implicit assumptions:

1.     That degradation patterns are broadly predictable.

2.     That connectivity is reliable.

3.     That regulatory reporting needs are generic and minimal.

Lithium mining seems to undermine all three.

2.1 Misaligned Degradation Assumptions.

In high‑salinity brines, dissolved solids can reach levels far above typical industrial water systems and divalent cations such as calcium and magnesium strongly promote scaling on heat transfer surfaces and flow paths.

This environment accelerates corrosion of metallic components and can drastically shorten the effective life of pump internals, valves, level instrumentation and evaporator surfaces compared with ‘standard’ water service.  Generic maintenance strategies that assume slow, linear degradation and mild service conditions therefore under‑specify inspection frequencies, cleaning intervals and component change‑out rules.

In hard‑rock spodumene operations, the primary stressors are ore hardness, abrasiveness and high‑energy comminution (the process of crushing and grinding rock to reduce its size).

Variations in ore characteristics between benches or phases can materially change crusher liner life, mill media consumption and conveyor wear rates, even when nominal throughput is constant.

Maintenance strategies that assume a fixed interval for liner changes or fully rely on OEM running‑hour guidance often either over‑maintain or suffer unexpected failures when the ore feed changes.

2.2 Master Data Structures That Ignore Chemistry And Geology.

Generic master data configurations frequently treat equipment purely as mechanical assets, with attributes like manufacturer, model, serial number and runtime statistics, but no systematic linkage to brine chemistry, ore type, or process conditions.

In lithium environments, however, variables such as ion composition, saturation indices, ore hardness, grind size and process temperature materially influence failure modes.

Without data integrations/interfaces and functionality to store, relate and trend these variables alongside maintenance histories, the system might not support meaningful predictive or condition‑based strategies.

For example, correlating pump seal failures with brine composition and operating temperature can reveal when chemistry shifts demand changes in materials, filtration, or dosing strategies.

Similarly, linking crusher wear to ore hardness and throughput can support dynamic adjustment of liner change‑out thresholds and spares stocking levels.

Generic templates generally do not anticipate these correlations and therefore fail to provide the fields and workflows needed to exploit them.

2.3 Connectivity Dependent Workflows In Remote Locations.

Many lithium projects, including those in remote regions of Australia, operate far from established infrastructure with limited terrestrial telecommunications and variable power reliability.

These conditions create a few challenges for cloud first CMMS deployments that assume continuous connectivity.

Mobile technicians and contractors often need to execute and close work orders in the field, yet cannot rely on stable online access.

When connectivity is intermittent, devices used to access CMMS, EAM, or ERP information must allow users to continue working offline.

They need to store all data entered locally and then upload it automatically once a connection becomes available.

This capability is essential for maintaining accurate work execution records, asset history, and parts transactions.

Offline first design, supported by robust local caching and asynchronous synchronisation, becomes a foundational requirement rather than an optional enhancement.

Without it, work execution slows, data becomes fragmented, and maintenance teams lose confidence in the system’s reliability.

Generic strategies that do not anticipate connectivity constraints tend to degrade into inconsistent processes and poor data quality.

Lithium operations that adopt offline first architectures maintain continuity in field execution and preserve the integrity of their asset information despite the limitations of remote environments.

2.4 ESG And Compliance Structures.

Lithium’s role in the energy transition places exceptional focus on water balances, emissions and community impacts, which translates into demanding monitoring and reporting expectations.

Brine operations may be required to track withdrawal volumes, evaporation losses, reinjection rates and water quality parameters across multiple monitoring points. Emerging DLE technologies add new streams, reagents and waste forms that must also be measured and reported for ESG disclosure frameworks such as GRI, SASB and TCFD.

Generic asset management setups sometimes lack data models and workflows to capture these environmental metrics in a structured, auditable way linked to assets and work activities.

This gap forces operators into a parallel ecosystem of spreadsheets and standalone tools, making it harder to demonstrate compliance, respond to regulator queries, or provide consistent ESG narratives to investors.

3.0 Operational Realities That Drive Failures.

Understanding the physical realities of lithium extraction helps clarify why generic maintenance strategies tend not to perform very well and what ‘good’ needs to look like.

3.1 Brine Evaporation: Chemistry Driven Degradation.

High‑salinity brines used for lithium recovery often contain complex mixtures of sodium, calcium, magnesium and other ions that promote scaling on evaporative and heat transfer surfaces.

As brine evaporates, concentration factors increase and scaling risks rise, which can choke flow paths, reduce heat transfer efficiency and foul sensors and level instrumentation.

Pumps, pipelines and valves in these systems also face corrosive attack that can rapidly degrade seals, impellers and metallic internals.

Effective maintenance in this context requires strategies that explicitly integrate chemistry‑based monitoring, scaling indices and variable inspection intervals driven by concentration and temperature rather than fixed calendar cycles.

Asset management setups should therefore support attributes such as brine composition, operating temperature ranges and scaling risk indicators at the asset level, with workflows that trigger cleaning, inspection, or material changes when thresholds are crossed.

3.2 Hard Rock Spodumene: Abrasive, Hardness‑Driven Wear.

Hard‑rock lithium mining from spodumene involves multi‑stage crushing, grinding and beneficiation of a moderately hard and abrasive ore.

The ore’s mechanical properties, including hardness and abrasion index, strongly influence crusher liner wear, mill energy consumption and media usage.

Calcination stages used to convert spodumene to more reactive phases introduce additional thermal stresses and equipment demands.

These realities mean that maintenance strategies should be closely tied to ore characteristics and process parameters, not just equipment running hours.

Asset management setups that capture ore type, hardness data and throughput alongside component condition and failure histories can support adaptive scheduling, such as shortening liner change intervals when particularly hard campaigns are processed.

Generic PM libraries that ignore these linkages tend to either mis‑time critical interventions or drive unnecessary maintenance during softer runs.

3.3 Remote Locations And Fragile Infrastructure.

Many lithium resources are found in deserts, high plateaus, or sparsely populated regions where road, power and telecom infrastructure are limited.

Seasonal access constraints, long supply routes and constrained local service capability increase the cost of unplanned failures and lengthen repair times.

At the same time, intermittent connectivity undermines real‑time access to cloud‑hosted CMMS, EAM or ERP platforms, especially for mobile workers.

An effective system design in this context prioritises offline‑capable mobile apps, store‑and‑forward synchronisation and local caching of critical asset and work data. It also incorporates supply chain integration that reflects long lead times and the need for strategic spares, particularly for specialised equipment exposed to high wear or corrosion.

Generic asset management setups that assume short lead times and constant connectivity tend to generate unrealistic PM’s and unreliable historic data.

3.4 Regulatory And ESG Intensification.

Regulators and stakeholders increasingly expect lithium operators to provide transparent, auditable data on water use, emissions and ecosystem impacts.

Water balance reporting for DLE and evaporation ponds may require quantifying withdrawals, consumptive use, recycling rates and discharge qualities at a high temporal resolution.

ESG frameworks also drive the need for consistent metrics on energy intensity, greenhouse gas emissions and land disturbance.

Aligning CMMS/EAM/ERP asset managment structures with these requirements means treating environmental monitoring points, sampling campaigns and key process parameters as managed assets with defined inspection frequencies, calibration tasks and data capture workflows.

When environmental data remains disconnected from asset and maintenance records, it is harder to demonstrate that equipment condition, process control and compliance are being managed in a coherent way.

4.0 Strategic Implications For Leaders.

For leaders in the maintenance, operations and engineering departments, the key implication is that ‘generic’ is itself a risk category in lithium environments.

4.1 Maintenance And Reliability Leadership.

Maintenance managers need to ensure that maintenance strategies and master data configurations are explicitly linked to lithium‑specific degradation mechanisms and this could mean:

1.       Grounding preventive and predictive programs in chemistry, geology and process conditions, not just standard reliability engineering data tables.

2.       Capturing relevant variables in asset records, including brine chemistry ranges, ore hardness bands, key process temperatures and scaling indices.

3.       Building inspection and condition‑based tasks that trigger on changes in those variables, such as increased scaling risk or shifts in ore feed hardness.

Without these structures, teams are limited to time‑based maintenance and retrospective analysis, which tends to produce higher reactive work and weaker equipment availability.

4.2 Operations And Production Leadership.

Operations leaders carry the production risk associated with system misalignment. In brine operations, failures in pumping, evaporation, or reinjection systems can disrupt water balances and evaporation cycles, leading to prolonged recovery times.

In hard‑rock circuits, unexpected failures of crushers, mills, or classification equipment can rapidly reduce output and drive costly overtime or emergency maintenance.

To manage these risks, operations leaders should insist on:

1.       Maintenance Strategies that reflect realistic equipment life under current chemistry and ore conditions.

2.       Integrated materials management that recognises long lead times and ensures critical & insurance spares are held for high‑risk assets.

3.       Feedback loops where production changes (new ore sources, process adjustments) are quickly reflected in maintenance strategies and system configuration.

4.3 CIOs And Technology Strategy.

Technology leaders must design architectures that are robust to connectivity constraints and aligned with data governance requirements for both operations and ESG. Priority areas typically include:

1.       Offline‑first mobile execution, with secure, reliable synchronisation to central CMMS/EAM/ERP platforms.

2.       Data models that link asset performance, maintenance events, environmental monitoring and ESG metrics in a consistent, auditable way.

3.       Integration with IoT sensors and process historians where appropriate, so that key condition indicators can drive automated triggers or analytics without excessive manual entry.

Cybersecurity and access control also become more important as remote monitoring, cloud hosting and third‑party analytics platforms are introduced into the mining ecosystem.

4.4 Organisational Readiness And Change.

Even the best‑configured system will fail if people, processes and governance do not align with lithium‑specific realities. Leaders should consider:

1.       Role‑based training that explicitly links asset care practices to the chemistry and geology of lithium operations.

2.       Governance structures that keep master data, PM libraries and environmental metrics under disciplined control.

3.       Continuous improvement cycles where lessons from failures, inspections and ESG reporting feed back into CMMS configuration and strategy.

This emphasis on organisational readiness is often the decisive factor that differentiates operations that extract value from tailored configurations from those that fall back into reactive habits.

5.0 Enabling Technology For Process Linked Maintenance Strategies.

Achieving the linkage between lithium‑specific degradation mechanisms and CMMS/EAM strategies requires an integrated operations technology stack, where real‑time process data from historians and DCS flows into asset systems via standard interfaces.

This setup leverages established tools like Process Historian (AVEVA PI System), Yokogawa DCS (CENTUM VP), and Operations Management Systems (OMS), connected to CMMS/EAM (e.g., IBM Maximo, Infor EAM) and ERP (e.g., SAP S/4HANA) through APIs, middleware, and data connectors.

5.1 Core Technology Components.

5.1.1 Process Historian (AVEVA PI System).

This captures high‑frequency time‑series data from plant instruments, such as brine TDS, temperature, pH, ore hardness proxies (e.g., power draw) and flow rates. PI Asset Framework (AF) models assets hierarchically and enriches tags with context like “Pump‑P101: Scaling Risk = f(TDS, Temp)”, making data contextual and queryable.

Just to explain, AVEVA PI System is a complete data management platform where the historian functionality is provided by its core PI Data Archive component.

Quick Structure Breakdown.

AVEVA PI System = Suite of Tools

├── PI Data Archive (the actual *historian* database – stores time-series data)

├── PI Asset Framework (AF) (models assets, calculates derived values like Scaling Risk)

├── PI Connectors/Interfaces (collect data from DCS/PLC)

├── PI Integrator (pushes data to EAM/ERP)

└── PI Vision/ProcessBook (dashboards)

When I mention ‘Process Historian (AVEVA PI System)’, I’m referring to:

1.       PI Data Archive as the time-series storage engine (the literal historian)

2.       Plus PI AF for asset context (brine chemistry → pump degradation models)

3.       Plus connectors that feed it from Yokogawa DCS

In practice: Your DCS streams brine TDS data → PI Data Archive stores it → PI AF calculates “Scaling Risk Score” → PI Integrator sends score to Maximo to trigger pump inspection.

The PI System is the full ecosystem; Data Archive is the historian heart. Both get casually called “PI” in industry conversation.

5.1.2 Distributed Control System (DCS, e.g., Yokogawa CENTUM VP).

This manages real‑time control loops, alarms and HMI visualization for brine evaporation, crushing circuits, or flotation cells.

It exposes process variables via OPC UA/DA or proprietary protocols for upstream archiving.

Yokogawa CENTUM VP is Yokogawa’s flagship Distributed Control System (DCS), it’s a robust, scalable platform for real-time process automation and control in industries like mining, chemicals, and oil & gas.

Core Purpose.

A DCS like CENTUM VP acts as the ‘central nervous system’ of a processing plant, managing thousands of control loops (e.g., brine pumps, crusher speeds, flotation pH) across distributed controllers rather than a single centralized computer.

It continuously reads sensors, executes control logic, adjusts valves/actuators, and presents operator interfaces, all with 99.99999% availability via redundant ‘pair & spare’ architecture.

Key Components & Structure.

CENTUM VP DCS = Complete Control Platform.

├── Field Control Stations (FCS): Distributed controllers (redundant CPUs) executing process logic

├── Human Interface Stations (HIS): Operator HMI screens for monitoring/control

├── Vnet/IP Network: 1Gbps redundant backbone (fiber/UTP) connecting everything

├── N-IO/FIO Modules: Universal I/O (AI/AO/DI/DO) with built-in signal conditioning

├── Engineering Workstation: Logic configuration & database management

└── Unified Gateway Station (UGS): Interfaces to Modbus, Ethernet/IP, Profibus, etc.

Lithium Mining Context.

1.       Brine evaporation: Controls pond levels, pump speeds, chemical dosing based on TDS/chemistry loops streamed to AVEVA PI historian.

2.       Hard-rock processing: Manages crusher load, mill power draw, flotation reagent addition with real-time alarming & trending.

3.       Remote deployment: FCS controllers can be field-mounted (Zone 2 hazardous areas) with N-IO baseplates handling intrinsic safety.

Data Flow To Historian/CMMS.

Sensors → N-IO → FCS Controller → Vnet/IP → AVEVA PI Data Archive

                  ↓

             Operator HIS (alarms, trends)

                  ↓

           OPC UA → PI Integrator → Maximo EAM (triggers)

Key Integration Points:

1.       OPC UA/DA: Exposes 10,000+ live process tags (brine TDS, crusher amps) to historians.

2.       Fieldbus Support: FOUNDATION Fieldbus, Profibus-DP for smart instruments.

3.       Pair & Spare Redundancy: Dual CPUs compare outputs continuously; <1ms failover.

Example Operator View.

HIS screen shows:

Pump P-101: Speed 75%, TDS 210k ppm ↑, Scaling Alert Active

↓ Auto-generate WO in Maximo via PI threshold breach

CENTUM VP has evolved since 1975, with R7 (2025) adding AI operator assistance, Windows 11 support, and virtualization to reduce server footprints.

In lithium plants, it delivers the real-time process backbone that feeds degradation data into historians for maintenance intelligence.

5.1.3 Operations Management System (OMS).

This Sits above DCS/historian to aggregate data into KPIs, trends, and operator workflows (often part of DCS or historian suites like AVEVA Operations Center). In lithium contexts, OMS dashboards might display brine chemistry trends or crusher wear indicators for operator awareness.

5.2 OMS Setup For Lithium Operations.

The OMS needs to be configured with asset hierarchies and calculated tags that reflect lithium‑specific drivers:

1.       Brine evaporation: OMS calculates scaling propensity indices (e.g., Langelier Saturation Index) from DCS inputs like conductivity, temperature, and ion proxies, alerting on thresholds that correlate with fouling.

2.       Hard‑rock processing: Tracks cumulative “abrasive ton‑hours” or hardness‑adjusted wear rates by combining throughput, power consumption, and periodic lab assays.

3.       Remote resilience: Edge processing and local buffering ensure data availability during connectivity gaps, with secure sync to central OMS.

This creates a unified view of process health that maintenance can reference without manual reconciliation.

This sub section refers to configuring a high-level operational oversight platform (like AVEVA Operations Center, Yokogawa Exaquantum, or DCS-integrated dashboards) to aggregate, contextualize and visualize process data specifically for lithium extraction challenges.

Core Purpose.

OMS sits above DCS and historian as an operations intelligence layer, turning raw tags (TDS, crusher power) into actionable KPIs, alerts, and trends for shift operators and supervisors.

In lithium plants, it bridges real-time control with maintenance foresight by calculating degradation proxies from process signals.

Key Lithium-Specific Configurations.

OMS Hierarchy for Lithium Plant

├── Area: Brine Evaporation Ponds

│   ├── KPI: Scaling Risk Index = f(TDS, Temp, pH) > Threshold → Alert

│   ├── Pond P-101: Level, Evap Rate, Chemistry Trends

│   └── Pump Fleet: Cumulative Runtime @ High Salinity

├── Area: Hard-Rock Processing

│   ├── KPI: Abrasive Wear Rate = f(Power Draw, Throughput, Ore Proxy)

│   ├── Crusher C-001: Liner Life Remaining (%), Ore Hardness Trend

│   └── Mill Circuit: Grind Efficiency vs. Media Wear

└── Site-Wide: Water Balance, Energy Intensity, Remote Sync Status

Setup Steps.

1. Calculated Tags for Degradation Proxies.

Brine Scaling Alert:

IF (TDS > 200k ppm AND Temp > 55°C for 24h)

   THEN Scaling_Index = (Ca_hardness * Mg_ratio * Temp_factor)

   OMS displays: "PUMP INSPECTION DUE" → DCS alarm → PI event → EAM WO

Crusher Wear Proxy:

Abrasion_Hours = Throughput_tph * Power_kW * Hardness_Factor (lab assay avg)

When Abrasion_Hours > Liner_Threshold → "LINER CHANGE SOON" dashboard

2. Lithium-Relevant Dashboards.

Operator Shift Handover Screen:

ACTIVE ALERTS: Brine Pump P-103 (Scaling Risk: HIGH)

TRENDS: Crusher C-002 power spiked +18% (hard ore campaign)

WATER BALANCE: Pond fill 82%, evap loss 1.2ML/day OK

MAINTENANCE: 2x pump WOs auto-generated via PI Integrator

3. Remote Lithium Site Resilience.

1.       Edge Computing: Local OMS server caches 72hrs data during satcom outage.

2.       Prioritized Sync: Critical KPIs (scaling risk, wear rates) sync first when reconnected.

3.       Mobile Access: Supervisors view KPIs via tablet (4G/satcom) 50km from site.

4. Integration Points.

DCS (Yokogawa CENTUM VP) → Historian (PI Data Archive) → OMS

     ↓ Raw Tags (1000s)              ↓ Compressed (100s)     ↓ KPIs/Alerts (10s)

     Brine TDS 210k ppm              Scaling_Index = 1.8    "INSPECT PUMPS"

Example Operator Workflow.

08:15: OMS dashboard flashes "CRUSHER LINER RISK: 92% consumed"

08:16: Operator checks trend: Ore hardness +22% vs baseline

08:17: OMS auto-emails planner: "C-001 liner change recommended by EOD"

08:18: DCS adjusts crusher speed -5% to extend life

08:30: Maintenance WO appears in Maximo with embedded OMS trend chart

Vendor Examples in Lithium Context.

1.       AVEVA Operations Center: Plant-wide KPI rollups, role-based views (operator vs. manager)

2.       Yokogawa Exaquantum: DCS-native, integrates CENTUM VP alarms with PI historian

3.       AspenTech APM: Process-focused, strong on calculated degradation indices

Outcome.

Operators see “Pump scaling risk: HIGH” instead of raw TDS=210kppm. Maintenance gets preemptive work orders with context.

Executives track fleet-wide wear trends across brine/hard-rock assets.

The OMS becomes the single pane of glass where process meets maintenance.

This setup transforms generic process data into lithium-specific operational intelligence without custom coding—just deliberate tag math and hierarchy design.

5.3 Integration Architecture To CMMS/EAM/ERP.

Data flows from OMS to enterprise systems via bidirectional interfaces, enabling context‑aware maintenance:

1.       Historian to EAM: AVEVA PI Integrator pushes enriched tags (e.g., “Ore Hardness Avg”) as meter readings or work triggers into EAM, updating asset contexts dynamically.

2.       OMS to CMMS: Event notifications (e.g., “High Corrosion Threshold Breached”) auto‑generate work orders or adjust PM intervals.

3.       EAM to ERP: Maintenance events and failure modes feed back for spares planning and cost allocation, closing the loop.

Middleware like OSIsoft PI Integrator, Kepware, or Azure IoT Hub handles protocol translation and data normalization for remote sites.

5.4 What Might An Integrated System Looks Like?

1.       Planner’s view (EAM): Work order for brine pump shows linked PI trends: ‘TDS spiked to 250k ppm last cycle, recommend seal inspection’ with embedded charts.

2.       Operator’s view (OMS/DCS): Dashboard flags ‘Crusher Liner Risk: High (Ore Hardness +15% vs. baseline)’, prompting feed adjustments before maintenance handover.

3.       Executive view (ERP): Reports forecast ‘$X increased spares due to abrasive campaign’, tied to process data for capex justification.

This architecture seems to be well supported in this current era of in mining (e.g., Barrick Goldstrike uses AVEVA PI for compliance and performance).

It shifts maintenance from reactive calendars to process‑aware intelligence without requiring custom development—just disciplined integration.

6.0 Terms And Abbreviations Used.

This section defines key technical terms and abbreviations used throughout the article, incorporating detailed explanations of core operational technology components relevant to lithium mining asset management.

6.1 Key Terms.

1.       Asset Framework (AF): Modeling layer in AVEVA PI System that organizes time-series data into equipment hierarchies with calculated attributes (e.g., “Scaling Risk Index” derived from brine TDS and temperature).

2.       AVEVA PI System: Comprehensive data management platform where PI Data Archive serves as the core time-series historian database, PI Asset Framework (AF) adds asset context, and PI Integrator pushes data to EAM/ERP systems.

3.       Bill of Materials (BOM): Structured list of spare parts and components linked to assets in CMMS/EAM for repair planning and inventory management.

4.       Brine Evaporation: Lithium extraction process where saline groundwater is concentrated in solar ponds, creating extreme scaling and corrosion conditions for pumps and pipelines.

5.       CENTUM VP: Yokogawa’s distributed control system (DCS) featuring Field Control Stations (FCS), Human Interface Stations (HIS), Vnet/IP network, and N-IO universal I/O modules for real-time process control.

6.       Condition-Based Maintenance (CBM): Maintenance triggered by real-time equipment condition data (e.g., scaling risk thresholds) rather than fixed calendar intervals.

7.       Distributed Control System (DCS): Plant automation backbone (e.g., Yokogawa CENTUM VP) managing thousands of control loops across redundant controllers with operator HMI interfaces.

8.       Degradation Mechanisms: Equipment wear processes unique to lithium operations, including brine-induced corrosion/scaling and spodumene ore abrasiveness.

9.       Enterprise Asset Management (EAM): Advanced CMMS platforms (e.g., IBM Maximo, Infor EAM) supporting reliability analytics, condition-based strategies, and whole-life asset optimization.

10.    Enterprise Resource Planning (ERP): Business platform (e.g., SAP S/4HANA) integrating finance, procurement, and supply chain with EAM data flows.

11.    Failure Modes and Effects Analysis (FMEA): Risk assessment methodology identifying equipment failure causes and mitigation strategies to inform PM libraries.

12.    Field Control Station (FCS): Distributed controller in CENTUM VP executing process control logic with pair-and-spare redundancy.

13.    Human-Machine Interface (HMI): Operator workstations (HIS in CENTUM VP) displaying process trends, alarms, and control functions.

14.    Master Data: Foundational CMMS/EAM information including asset hierarchies, task templates, custom attributes (e.g., “Ore Hardness”), and BOMs.

15.    N-IO: Yokogawa’s universal I/O system with built-in signal conditioning for field instruments in hazardous areas.

16.    Operations Management System (OMS): Intelligence layer (e.g., AVEVA Operations Center, Yokogawa Exaquantum) aggregating DCS/historian data into lithium-specific KPIs like scaling risk and wear rates.

17.    PI Data Archive: Core time-series database component of AVEVA PI System storing compressed process data (tags) with timestamps and quality flags.

18.    Process Historian: High-frequency time-series database optimized for industrial sensor data (e.g., AVEVA PI Data Archive, Aspen IP.21).

19.    Reliability Centered Maintenance (RCM): Structured methodology defining maintenance strategies based on failure consequences and detection methods.

20.    Spodumene: Lithium-bearing hard-rock mineral requiring crushing, grinding, and calcination, creating variable abrasive wear patterns.

21.    Vnet/IP: Yokogawa’s 1Gbps redundant industrial network backbone connecting DCS components.

6.2 Abbreviations.

7.0 Conclusion.

Lithium extraction environments do not necessarily require new CMMS, EAM, or ERP technologies, but they do benefit from deliberate integration of proven operational systems that address the shortcomings of generic, template driven strategies.  

Brine operations and hard‑rock spodumene circuits create corrosion, scaling and abrasive wear patterns that DCS platforms such as Yokogawa CENTUM VP capture in real time.

Process historians like AVEVA PI System archive and contextualise these signals, and Operations Management Systems convert them into maintenance intelligence that reflects actual operating conditions.

Remote lithium sites with fragile connectivity require offline first EAM configurations supported by process aware data flows rather than calendar based assumptions.

Increasing ESG scrutiny demands auditable environmental metrics linked directly to asset performance records, not isolated spreadsheets or manual reporting.

Organizations that persist with disconnected, generic maintenance strategies and overall asset management setups face ongoing reactive maintenance, unreliable degradation forecasting and fragmented compliance reporting.

Those that do the clever work (although this comes at a cost) and integrate their DCS, historian, OMS, EAM and ERP systems around lithium specific realities such as brine chemistry thresholds, ore hardness variability and water balances achieve predictive maintenance capability, resilient remote execution, and defensible sustainability metrics.

The technology required to support this already exists across all major vendors. Success belongs to operations that configure it with discipline, linking process reality to asset strategy across the full lifecycle of their lithium infrastructure.