MINERALS & METALLURGICAL PROCESSING VOL. 21, NO. 1 • FEBRUARY 20041

Practical integrated metallurgical accountingsolutions for supporting production decisions

F. FlamentCEO, Algosys Inc.

M.C. MoundQuality Systems, CPM Solutions, ABB, CH

C. ColbertHead, CPM Solutions, ABB, CH

L. FradetteR&D Director, Algosys Inc.

AbstractMetallurgical accounting in the mineral processing industry transforms accumulated process data, includingflow rates and assays, into a coherent information format. This is used to rapidly provide production reportingand key performance indications. Comprehensive and coherent data must be a component of any systemexpected to accomplish reconciliation in the mass-balancing process. Quality issues are thus improved withsuch desired methods via a mass-balancing engine utilizing the results of measurements acquired by variouson-line sensors, analyzers and sophisticated laboratory equipment. Metallurgical accounting, when accom-plished through such an engine, provides high-value information in a simple, straightforward and simplifiedmanner. Exceptional utility is then transparently available to authorized personnel for access to metallurgicalaccounting reports, production reports, mass balance results, performance indicators, and so on. This paperdiscusses how such information can be used to “drill down” into each result set by consulting the datahierarchy to evaluate and validate calculations and input data used to produce the specific results beingqueried. The power of such a system resides in the full and true integration of all production and laboratorydata. What is integrated are the data thus acquired via a single user interface, a single database, a singleconfiguration tool, etc. From this comes the derivation of contextual information and its maintenance. It ispossible to reduce metallurgical accounting and production reporting from possibly hours or even days to amatter of minutes. The result is maximization of information reliability and availability from masses ofotherwise discrete and difficult to access key data.

Key words: Metallurgical accounting, Knowledge management, Plant processing data

IntroductionIntegration in knowledge management systems. The op-eration and management of an industry-specific mineral pro-cessing plant data system requires the evaluation of data fromvarious sources to get the most complete picture on theperformance of the plant. The raw data available from thedifferent sources are typically not consistent because of lim-ited accuracy of the installed instrumentation. Some of thevariables might be even not be available because the costs ofinstruments cannot easily be justified or because they cannotprovide direct measurements.

By using an integrated data-management system, the in-consistency of the different measurements can be automati-cally resolved by mathematical methods or can, at least, behighlighted for further corrective or adjustment actions. Theseactions may be in the form of recalculations, formulae adjust-ments, or simple validation.

Additionally, the integrated data management system au-tomates the flow of data between different departments of themine production units, various data sources and informationconsumers throughout the site. Such an integrated data man-agement system would make all information available in realtime to those authorized users connected to the intranet or thecompany’s wide area network (WAN).

Knowledge managementStructure and modules. Knowledge Manager (KM) is amodular solution (Fig. 1) and is the environment for a numberof industry applications. It can be tailored to meet a company’sspecific requirements and can be expanded to incorporate upto several hundred users and multiple production plants. Thebasic system comprises all functions and components forcommunication and data collection, data storage and presen-tation. There exists a basic set of templates and ready-to-use

Preprint number 03-130, presented at the SME Annual Meeting, Feb. 24-26, 2003, Cincinnati, Ohio. Revised manuscriptreceived and accepted for publication September 2003. Discussion of this peer-reviewed and approved paper is invited andmust be submitted to SME Publications Dept. prior to Aug. 31, 2004. Copyright 2004, Society for Mining Metallugy andExploration, Inc.

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Plant people and IT (information technology): KnowledgeManager is a solution for and by plant people. The concept hasbeen designed, built and maintained by industry specialiststogether with IT development experts. Though using the latestand mainstream IT technology, the key to efficiency andutility relies on the principle that a Knowledge Manager isimplemented for daily use by non-IT experts.

A solution using Knowledge Manager is primarily focusedon production mine/plant management and personnel. Thesystem is often “owned” and maintained by the users: plantmanagement, production, maintenance or automation depart-ments, especially in operations with limited or no IT depart-ments. After fine-tuning, the system is often mainly used byadministrative personnel as part of assisting plant manage-ment functions. Knowledge Manager system is built specifi-cally for such a profile of ownership.

Information integration.Production plant information management: Functionally,

KM consolidates isolated data from a multitude of sub-systems, turns it into meaningful information and producesthe results for a variety of different purposes:

Via a Web-based user interface, results are directly pre-sented to users in analyses, reports, lists, trends and graphs forboth reporting and making plant decisions. Transformation totransactional data produces direct input for BDW (businessdata warehouse (BDW) andenterprise resource planning (ERP)systems. Key performance indicators (KPIs) are then pro-cessed for monitoring, reporting and storage in BDWs

To perform these functions, KM integrates and connectseasily with other common plant IT systems. As a result of itscentral role in plant information management and its versatileconnectivity, the system often acts as the integration platformbetween these systems. This provides the environment forproduction accounting purposes. KM handles informationconsisting of process and production values, calculations,energy consumption, plant emissions, running hours, eventsand alarms, raw material, fuel, product quality measurements,production stops and low rate production runs, etc. Most ofthis information requires reliability, consistency and is oftentime critical. Often the availability of the information isbusiness critical and is required for operating permissions(Fig. 3).

By proactively using this information, the gap can bebridged between what is happening in the process and what ishappening on the business side. KM not only makes relevantprocess and lab data available to production people, it alsomakes it available throughout the organization. By incorpo-rating process, business, maintenance and even planning data,KM is a critical component in total plant optimization andmanagement. Functionally, what is also provided is criticalinformation for effective planning of limited resources (silos,shops, stocks, energy, etc).

Metallurgical accounting — general. Metallurgical Ac-counting, as perceived herein, ensures full coherency ofavailable data while improving data quality through a recon-ciliation process of mass balancing.

The use of an engine to accomplish this allows for thevarious data sources within a mine/plant to be fully used,validated and improved. Consequently, the resulting informa-tion contributes added value to published information. Mea-surements that are often acquired at high costs with on-linesensors and analyzers, or sophisticated laboratory equipment,are used to their full extent.

Figure 1 — Knowledge Manager modular structure.

Figure 3 — Relationships between systems in a mine/plant.

Figure 2 — Knowledge Manager and IT systems.

applications for a wide range of reports and lists, whicheffectively supplements this system.

The basic version of such a system can be expanded by avariety of software modules to keep pace with specific re-quirements. There are specialized modules for production andprocess management, maintenance support, statistical pro-cess control, laboratory management, production accounting,etc. (Fig. 2).

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Metallurgical Accountant™Overview. In modern processing industry, maximum produc-tion is not enough anymore. Everyone must think in terms ofmaximum productivity. Key issues then become reliability,flexibility, and adaptability. Decisions are based on limitedinformation, sorted and extracted from the huge amount ofnumbers available in process and management databases. Tomaintain their confidence in the numbers they use on a dailybasis, managers and engineers must rely on systems thatoutput performance indicators in a systematic approach, basedon proven technologies and reflecting a clear image of thereality. Metallurgical Accountant offers to managers andengineers this reliability, flexibility and adaptability.

Production accounting. One of the most time-consumingand tedious tasks dedicated to metallurgical engineers is thecomputation of production numbers used by the accountingdepartment. This task requires the conciliation of hundreds ofvariables at the same time and often results in an oversimpli-fication of the problem. Metallurgical Accountant is a soft-ware suite especially developed to fulfill the needs of auto-matic production reporting with reconciled information. Oneof its major features is its ability to run data reconciliationproblems in background and only outputs results throughreports and emails. Daily or shift material balances are auto-matically set up, run and reported to the configured addresses.A final step using engineer validation can be introduced in thechain.

Process monitoring. Everyone knows that online analyzerscan be faulty, but no one knows when they become in a faultystate. Only process upsets can lead to the detection of badlyoperating analyzers. Indicative material balance (IMB) caneffectively support process monitoring and help maintain thereliability of the numbers used in performance indicatorscalculations. This type of material-balance problem runs atrelatively high frequency, in the order of minutes, and outputsthe results directly in the real-time database supporting pro-cess operation. Reconciled information is then available forprocess operator and engineers.

What is meant by data reconciliation? Data reconciliationthrough mass balancing is the specific means by which infor-mation is made useful and functional. In the mine environ-ment, sensors and instrumentation are rationalized in a man-ner so as to generate minimum levels of data redundancy. Thisenables mathematical improvement of data (supported bymass conservation equations). Data reconciliation via thismeans (mass balancing) improves accuracy of process andassay data. This resulting reconciliation benefits the entireplant operation by providing access to those variables/valuesthat cannot be directly measured. Mass balance calculationsuse flow measurements and assay data to improve on inputdata relative to quantity and quality information in either real-time and/or reporting periods.

In most mineral processing industries with increasinglystringent economic constraints, analyses on fractions and sub-fractions have become part of the daily routine. Few datareconciliation solutions fully support these types of measure-ments and their complex inherent mass conservation con-straints.

Reconciliation solutions pioneered by applications de-rived from other sources (such as the power and petrochemi-cal industries) are, however, inadequate to reconcile Level IIIand Level IV data. These would include, for example, chemi-

cal analysis of each size fraction of a solid sample and finenessanalysis of the residue of the size fraction from a solid sample.

Data reconciliation with Bilmat™.Description: Before discussing the algorithm of Bilmat

and its functionalities, it is important to understand the struc-ture of the measurements and the constraints associated tothem. The data structure takes its root with the stream andrepresents Level I-type information (Fig. 4). The Stream canbe split into phases. Bilmat handles two phases, 1 and 2, butdoes not allow for analyses in Phase 2. Complete and incom-plete analyses can be handled for Phase 1. They correspond toLevel 3 analyses. An Incomplete analysis provides informa-tion about a fraction of the bulk phase flow and leaves the restunknown. A Complete analysis provides information aboutthe entire bulk phase flow and allows for more detailedanalyses at Level IV, replicating the structure of Level 3. It isobvious that the complete data structure imposes some con-straints on the analyses and their relations. Bilmat is built tointrinsically handle all the structure constraints imposed bythe most complex dataset.

The measurements at the various levels pertain to two largefamilies: process and laboratory measurements. The lower thelevel, the higher are the chances the measurement comes fromthe process and the exact opposite with the highest level.Hence, Level I and II are basically flow rate measurements.The phase fraction is also part of these levels. Level IIImeasurements are chemical analyses or distributions (size ordensity). In a case where there are many phases in a stream andthey are not analyzed individually, they can be assimilated toa complete analysis. Level IV measurements are of the sametype as Level III measurements but are made on each or oneof the fractions of the complete analysis of Level III. Here, itis very important to handle the possible coherency constraintsbetween Level III and IV analyses.

Levels II and I are linked and cannot be treated individuallybrcause the phase fraction bridges them. Where applicable,conservation equations are written for the stream, Phase 1 and2 conservation as well as the equations for the phase fractioncontent of the stream.

Level III expresses the conservation of the individualanalyses made on Phase 1. Level IV consists in specificanalyses performed on the various fractions of a complete,“mother” analysis.

As stated in the mass balance document, the techniquesupporting both Matbal and Bilmat requires the error modeli.e. the error attached to each measurement. This is often themajor challenge in obtaining a reliable mass balance result

Figure 4 — Structure of the available measurements.

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and explains why effort has been made to provide users withvaluable tools to develop a good error model. The Visualiza-tion sheet and the validation module are two valuable tools tosupport the users in the development of the appropriate errormodel.

The Bilmat Engine. The BILMAT algorithm, originallydeveloped at Laval University following the work of Hodouin(Hodouin Everell, 1980; Hodouin et al., 1981; HodouinFlament, 1989) is based on fundamental principles that takeinto consideration the statistical properties of the data errorsand all the mass conservation equations implied in a flowsheet. The algorithm solves the system of mass conservationequations in the case where the measurements are redundant- that is in a number greater than needed to calculate themissing data. Because the measured values do not verify themass conservation equations due to errors in measurement,the algorithm adjusts them. The adjustments are made as smallas possible and in agreement with the reliability of the mea-surements. Though, the adjustment realized on a measure-ment qualified as poor (i.e., obtained by poor sampling or pooranalysis) can be larger than that on a measurement qualifiedas precise.

Generalized least-squares method: Many methods of ma-terial-balance computation assume that the experimental val-ues of the assays are their best estimate. Below, a method isdiscussed in which the experimental values are also adjustedby an amount such that the sum of squares of all adjustmentsis minimum, whence the name “least squares” method.

If best estimates x and W are associated to each assay xand flow rate W, the maximum-likelihood estimators for anormal population of x and W are such that

x x W W−( ) + =( ) =∑ ˆ ˆ2 2minimum (1)

This estimator is also called an “estimator of minimum vari-ance.”

In a process where x and W have been measured, one may

want to calculate the best estimates x and W . These can beobtained by solving all valid mass balance equations in theflowsheet.

The notion of data redundancy is sometimes very subtleand hard to grasp at first. In particular the Bilmat presentationis based on the concept that the only true variables to beestimated are the flow rates and that the number of equationsis therefore usually much larger than the number of un-knowns. The solution to this problem also happens to producebetter estimates of the assays. The MATBAL presentation,from the onset, classifies assays and flow rates as problemvariables for which better estimates are sought. In that casethe number of equations is smaller than the number ofvariables. Nevertheless, both programs solve essentially thesame problem.

The Sensitivity Analysis in Bilmat (Hodouin et al., 1988):No material balance problem is complete without a valuebeing calculated for the precision of the flow rate estimatesand, ideally, for the resultant adjusted assays. In other words,the confidence interval of the material balance results must beestimated.

Therefore, the benefits of a sensitivity analysis are:

• assessment of the error on the estimates and• reflection on the experimental results and on the error

model used to perform the computation.

A following basic principles of statistics must be reviewedto better understand the sensitivity analysis:

• For a normally distributed variable, the mean value andthe standard deviation of a series of measurements(sample) are the maximum-likelihood estimates of thedistribution parameters. They are estimates of mini-mum variance.

• It is assumed that the residuals and the errors arenormally distributed. The estimates being of minimumvariance, the variance of the calculated variables issmaller than, of equal to, that of the experimentalvariables. Similarly, the variance of the residuals issmaller than that of the errors.

The sensitivity analysis consists in studying the relation-ship between the error model of the experimental data and thatof the calculated estimates. The question is: How sensitive arethe results of a mass balance computation to errors in mea-sured variables?

A detailed and informative description of quality of perfor-mance indices calculations based on reconciled informationcan be found in Hodouin and Flament (1991)

Besides providing the evaluation of the reliability of theresults, the sensitivity analysis allows a classification of thedata with respect to the amount of information they contain.This aspect of the analysis is very important because it helpsto define a more appropriate sampling strategy. The measure-ment corresponding to very informative data should be verycarefully performed, at the same time variables that containvery little information can be eventually ignored in the datacollection step.

Finally, sensitivity analysis aims at detecting outliers bycomparison of the adjustment calculated by BILMAT to theassumed error model. However, it must be pointed out that theconcept of outliers is relative to the assumed error model, asa particular value can behave as an outlier only because theerror model is inadequate due to a bias or an under-evaluatedvariance.

Information flow. Mass balance sessions, if of prime impor-tance, are not the ultimate goal of Metallurgical Accountant;its main role is to be in such a position that many managementsystems can use and benefit from the very reliable datagenerated within the product. This is why information, afterevery mass balance completes, is made available for any typeof reporting (Fig. 5).

The metallurgical accounting task can be divided into thefollowing six distinct steps: data acquisition, filtering, prepro-cessing, data reconciliation, validation and reporting. Everystep has its own purpose, task and intelligence. The centralpart is obviously the data reconciliation step with three differ-ent steps to properly setup the problem with reliable values.Validation consists of a recommended step following recon-ciliation and within which an engineer electronically signs theresults before they become available for publishing. Duringvalidation, all Bilmat modules are available to support thework such as the validation module aiming at the detection ofbad measurements and/or badly adjusted values (Flament etal., 2001).

What is meant by data reconciliation? Data reconciliationthrough mass balancing is the specific means by which infor-mation is made useful and functional. In the mine environ-ment, sensors and instrumentation are rationalized in a man-

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ner so as to generate minimum levels ofdata redundancy. This enables mathemati-cal improvement of data (supported by massconservation equations). Data reconcilia-tion via this means (mass balancing) im-proves accuracy of process and assay data.This resulting reconciliation benefits theentire plant operation by providing accessto those variables/values that cannot bedirectly measured. Mass balance calcula-tions use flow measurements and assay datato improve on input data relative to quantityand quality information in either real-timeand/or reporting periods.

In most mineral processing industrieswith increasingly stringent economic con-straints, analyses on fractions and sub-frac-tions have become part of the daily routine.Few data reconciliation solutions fully sup-port these types of measurements and theircomplex inherent mass conservation con-straints.

Reconciliation solutions pioneered byapplications derived from other sources (such as the powerand petrochemical industries) are, however, inadequate toreconcile Level III and IV data. These would include, forexample, chemical analysis of each size fraction of a solidsample and fineness analysis of the residue of the size fractionfrom a solid sample.

Reconciled information.Indicative mass balance — Description of IMB: The “in-

dicative material (mass) balance” functionality is an addi-tional feature of Metallurgical Accountant. The purpose ofthis indicative material balance is to provide on-line processmonitoring with reconciled values. Therefore, there is novalidation or manual interaction within the IMB process. It isa fully automated mass balance application.

• At a configurable period, 15 minutes for instance, theMetallurgical Accountant conductor starts the account-ing cycle.

• The data acquisition module performs a data gatheringinto the real-time database. As for the accounting fea-ture, filtering and pre-processing are performed. Theyare, however, configured for the specific task of indica-tive mass balance.

• Data reconciliation is executed in the same fashion asfor the accounting purposes.

• No e-mails are sent for validation. The results areimmediately posted for process monitoring usage. Per-formance indicators and some unmeasured streamsanalysis of flow rates become available with a highdegree of reliability without being measured.

• Upon completion, the results can be written into theMetallurgical Accountant database, reported or writteninto the real-time database of the process.

• The cycle is completed and ready to the next indicativemass balance.

Metallurgical Accounting — Description of PMB:

• Upon triggering from the Metallurgical Accountantconductor, the data gathering and filtering engine scans

the target databases for daily production data. Whenavailable, the raw assays and the production data fromthe real-time database are transferred into the Metallur-gical Accountant database. If manual entries are re-quired for a given scheduled mass balance, it is vali-dated at this stage.

• The required data filtering is performed. Filtering canbe implemented to remove bad readings from the bal-ance. A bad reading can be totally excluded, its valuealtered according to preconfigured values or a replace-ment value can be calculated.

• Preprocessing may then be required in case of dynamicdata reconciliation. It is also required to transformcertain types of readings and force the fit into a massbalance problem.

• Bilmat™, the operative on-line PMB engine, reconcilesthe process data. The supporting Excel workbook isbuilt according to the plant configuration during theconcerned accounting period.

• The conductor sends an email to the process engineerfor validation. The current Excel reconciliation docu-ment is sent as an attachment. The data processing cycleis now broken. Only the validation from the dedicatedengineer can make the processing cycle continue.

• The process engineer receives the email with its Excelworkbook attachment advising him that the raw andreconciled data are ready for validation. From anyworkstation, a stand-alone network version of Bilmat isused to validate both the raw and reconciled values. Anynumber of material balances can be executed locallywithout interfering with the database. The raw assayscan be manually modified, and reconciled values recal-culated at will.

• When the process engineer is satisfied with the results,a global comment about the validation can be added.Comments can also be entered on a per-value basis.The validated raw assays and the new reconciledvalues are then committed to Metallurgical Accoun-tant database.

• The final production report is generated, viewed andprinted if desired. Emails are sent to selected addressees.

Figure 5 — Information flow within the accounting module.

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• The production mass balance cycle is now completedand ready for the treatment of the next period.

Inventory management. The inventory management systemkeeps track of material flows and the inventory of raw mate-rials, fuels, and intermediate and final products. This isaccomplished by taking the data on production and consump-tion from the process control system, and data on shipments/received material from the dispatch system. This informationis used to keep the technical (i.e., calculated) inventory data upto date. The inventory management system supports stockmodeling with grade blending, in, for example, tanks andmixing stock beds. In other words, the inventory managementsystem tracks quantity and the chemical composition of pro-cess materials as well as finished product in various storagelocations.

The inherent inaccuracies of reports of material flows areaccumulated over time in calculated technical inventory data.To correct these and other discrepancies, the actual stocklevels are measured or estimated regularly (e.g., once a month)and entered into the system in order to adjust these stocklevels. Adjustments of the stock levels are further used to fine-tune and balance production and consumption data (Fig. 6).

To support such adjustments, the ideally configured sys-tem provides interactive reports and graphs with drill-downcapabilities on all material flows to and from stocks. Thesehigh value-added functions are part of our system and supportthe user in a manner designed to quickly detect and resolveinconsistencies in the inventory data with maximum flexibil-ity tailored to specific plant needs.

The benefits of the knowledge-based expert optimizingsystem as described here are:

• consistency of process operation,• higher operator performance,• improved productivity,• better product quality,• savings in energy and maintenance/environmental costs,• ease of maintainability and• long-term process enhancement.

LIMS: General functionality. KM LIMS is designed tomanage the quality information according to good lab prac-tices. This management covers tasks from the routine qualitycontrols to final product quality controls and compliancecertificates. The function of LIMS is to provide advancedmeans to document quality information, make it easily acces-sible, transparent and to increase the knowledge of plantpersonnel of what is happening in the process. The systemprovides the basis for direct process and quality control, forday-to-day management decisions in production and market-ing and for long term improvements and developments.

LIMS includes functions such as automatic and manualdata collection, data validation, data consolidation, long-termstorage, calculations, sample logging procedures, sample sta-tus reports, work lists, standard and compliance reportingfeatures, easy to use means for trending, graphing and optionsfor EXCEL and SPC applications.

Tasks can be divided into two main categories (Fig. 7):

• information management tasks and• laboratory tasks

Quality-related information thus becomes an integral partof a plant information system. The quality information col-lected from the raw materials, process and final products can

be divided into the following three groups:

• quality analysis to provide timely feed-back to production process control,

• raw material and fuel analysis for re-cording and adapting production qual-ity planning and

• end-product quality analysis for com-pliance reporting

LIMS in a collaborative solution. Ex-pensive instruments (e.g., chromato-graphs) are often installed only in onelaboratory of an enterprise and performanalysis services for multiple productionplants. These production plants are usu-ally geographically spread. In such a situ-ation, samples taken from multiple pro-duction plants are sent to the plant withthe specialized analysis equipment. How-ever, these plants could all have their ownindependent LIMS server because most

Figure 6 — Adjustments vs. reconciliation in the datahierarchy pyramid.

Figure 7 — Overview of KM LIMS tasks.

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of the analyses are done locally in theproduction plants.

When a sample is registered in Plant Aand sent to Plant B for certain analyses,collaboration between KM LIMS A andB is required (Fig. 8). The workflow ismainly executed in the lab from Plant B,while the information requirement ismainly in Plant A. KM LIMS supportssuch implementations. Obviously a com-munication infrastructure between the twolocations must be available.

Integrated solution for pro-duction decision: The CVRD –Sossego CaseSossego Project, CVRD, Brazil: Over-view. Sossego will implement an inte-grated data-management system using au-tomatic collection of data from multiplesources in the plant. The data are thenused by various copper-specific softwareapplications that operate as part of theintegrated system (Fig. 9). They include:

• data reconciliation,• metallurgical accounting,• production and inventory manage-

ment,• maintenance support and integra-

tion to Maximo and• LIMS.

This is the latest installation of CVRD.It will have an “integrated” plant infor-mation management system, i.e., a PIMS/MES than runs on a single server with onesoftware infrastructure, database, etc.where the five copper specific mineralsapplications are installed as additional“software plug-ins.” Most previous plantshave installed separate point solutions orapplications running on separate servers,which can require complex interfacingand upgrading. This integrated solutionwill achieve a lower overall life cyclecost.

Data reconciliation. The functionalityof this application is data reconciliationthrough mass balancing. Data reconcilia-tion via mass balancing improves accuracy on process andassay data

Metallurgical accounting. Metallurgical Accountant inSossego’s PIMS provides integration of all production andlaboratory data to a single user interface, using a singledatabase and engineered by a single configuration tool. Func-tionally, this procedure results in the reduction in time neces-sary for using these data for reporting and control from severalhours, or days, to minutes. Information reliability and avail-ability is maximized.

Production and Inventory Management. The inventorymanagement system keeps track of metal from the entry of ore

materials into the crushing area through to the Cu concentratestockpile. The system takes the data from the process controlsystem and shipments and received material from the dispatchcircuit. This information is used to keep the technical (i.e.calculated) data up to date.

Maintenance support. The maintenance management sys-tem used at Sossego is MaximoTM that requires information,available in the PIMS. The first task of the maintenancesupport module is to administer the relevant run time and loadfactors on plant equipment. (This is required for condition-based monitoring and maintenance planning.)

The second task is to track all the production stops andbreakdowns in the plant. These stops are classified using

Figure 8 — Collaborative LIMS.

Figure 9 — Sossego case.

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predefined cause and effect diagrams. In addition to produc-tion stops, low productions rates can be recorded as events. Allunplanned stops that require repair or maintenance are up-loaded to the maintenance management system.

LIMS at Sossego. The (laboratory information managementsystem (LIMS) at Sossego supports the entire laboratoryworkflow from scheduling of samples, preparation, ID en-tries, recipes for analytical procedures, special requirements,tracking, results, approvals and validations through accep-tance and process inputs.

ConclusionModern mineral processing employs various forms of metal-lurgical accounting for a variety of critical applications.Availability of information required to economically managethe multiple-process task units in an integrated operationdepends on speed and accuracy as well as a reliable means ofcoherency. To maximize the benefits of acquisition of qualityresults derived from both in-process and centrally locatedsensors, an engine for gaining detailed metallurgical account-ing information is required. Integration of production andquality data via such an engine using one common databaseavoids issues of difficulties in reconciliation and maintenanceof the many types of data collectors throughout the operation.The benefits are gains in productivity and quality operations

provided by means of such an engine reduces the time re-quired for access. Thus, reports and ultimate utilization ofneeded critical information are available in a matter of min-utes instead of hours. This leads to real management capabil-ity, production economies and profitable operation. Accessi-bility and, therefore, immediacy of useful information alsoeliminates the time-consuming resolution otherwise requiredfor dealing with massive data and their relative complexity ofdiscrete data sources.

ReferencesHodouin, D., and Everell, M.D., 1980, “A hierarchical procedure for adjustment

and material balancing of mineral processing data,” Int. J. of MineralProcessing, 7, pp. 91-116.

Hodouin, D., Kasongo, T., Kouamé, E., and Everell, M.D., 1981, “An algorithmfor material balancing mineral processing circuits — Application to commi-nution, desliming and flotation circuits,” CIM Bulletin, September, pp. 123-131.

Hodouin, D., and Flament, F., 1989, “New developments in material balancecalculations by the mineral processing industry,” Preprint 89-147, SMEAnnual Meeting, Las Vegas, Nevada.

Flament, F., Labranche, M., and Fradette, L., 2001, “Data validation in a contextof data reconciliation,” CIM 2001, Quebec City, Paper TUPM4-110.

Hodouin, D., Flament, F., and Bazin, A., 1988, “Reliability of material balancecalculations — A sensitivity approach,” Minerals Engineering, 2 (2), pp.157-170

Hodouin, D., and Flament, F., 1991, “Influence of data collection and condition-ing strategies on the significance of performance indices in mineral pro-cessing plants,” Proceedings of the International Symposium: Evaluationand Optimization of Metallurgical Performance, SME/AIME, Chapter 19,pp. 195-208.