WO2020198794A1

WO2020198794A1 - Process monitoring system

Info

Publication number: WO2020198794A1
Application number: PCT/AU2020/050323
Authority: WO
Inventors: Jonathan GAMEZ; Warwick Smith; Matthew Magee
Original assignee: Octavius Partners Pty Limited
Priority date: 2019-04-03
Filing date: 2020-04-02
Publication date: 2020-10-08
Also published as: AU2020251048A1

Abstract

A method and system for remotely monitoring a mine or mineral processing plant is provided. The system includes: a plurality of data sources at a mine or remote mineral processing plant site, the data sources including real-time (or near real time) data sources; a data assimilation node at the mine or remote mineral processing plant site, configured to assimilate data from the plurality of data sources as it is received; and a server, located remotely from the mine or remote mineral processing plant site. The server is configured to receive the assimilated data at or near real time, and transform the data into measure sets, each measure set relating to a state of the plant at the corresponding period of time, and comprising the data relating to that point of time and historical data for data points in which data does not exist, or only partially exists, for that period.

Description

PROCESS MONITORING SYSTEM

TECHNICAL FIELD

[0001 ] The present invention relates to remote monitoring and analysis of industrial processes, and in particular, although not exclusively, to remote monitoring of mines and mineral processing plants.

BACKGROUND ART

[0002] It is estimated that over 70% of mineral processing plants are not operating efficiently. In particular, production output of the such mineral processing plants is sub-optimal when the plant is in operation, and production interruptions are common. It is well established that improving the efficiency of a mineral processing plant is associated with significant economic impact, and thus highly desirable.

[0003] One approach for increasing efficiency of such mineral processing plants is to utilise historical data of the plant to make informed decisions about the future operation of the plant. In such case, data is captured for the plant, which is analysed to identify potential areas for improvement.

[0004] Modern mineral processing plants generate large amounts of data, from a variety of different sources and in different formats. As such, it is common to extract a subset of such data for a limited, and relatively short period of time, and then perform one-off analysis on that data. Such analysis is generally labour intensive, and the outcome of the analysis is generally a report identifying potential areas of improvement.

[0005] While such data may provide some general insight into the operation of the plant, it provides such insight well after any problems have occurred. As a result, it is not possible to intervene when a problem is occurring, to mitigate or avoid the problem, or to take advantage of an opportunity when the opportunity presents itself.

[0006] While such analysis may help improve efficiency, as it is a labour intensive one-off analysis, it is generally not able to be performed in real time, and as such, problems and inefficiencies are often not identified until well after they have been introduced. Furthermore, it is difficult to evaluate any improvements in efficiency from changes, as any comparable analysis is likely to be performed much later.

[0007] There is generally a desire to analyse such plants in or near real time. A problem, however, is that a mineral processing plant may include up to 30,000 tags, from which data is constantly generated. Simply handling such amounts of data is problematic, let alone in real time. In practice, this data is often couriered from the plant site on hard drives for later analysis. A further problem is that the amounts of data make it impractical to engage third party expertise, let alone in or near real time.

[0008] A further problem with analysis of such data is that it is very complex. For example, data from a mineral processing plant generally includes a varied combination of high frequency data (e.g. sensor data), low frequency data (e.g. infrequent lab data), and non-temporal data. Such mixture of data makes one-off batch processing complex, let alone any real-time analysis. Current methods are unable to process such data at the rate in which it is generated.

[0009] One strategy may be to utilise a fewer number data points in an attempt to enable faster analysis of the data. However, the use of such limited sets of data is inaccurate, and as such, is unable to provide valuable insight into the plant.

[0010] As such, there is clearly a need for an improved methods and systems for monitoring processes, such as those performed in mineral processing plants.

[001 1 ] It will be clearly understood that, if a prior art publication is referred to herein, this reference does not constitute an admission that the publication forms part of the common general knowledge in the art in Australia or in any other country.

SUMMARY OF INVENTION

[0012] The present invention is directed to methods and systems for monitoring processes, such as processes in mineral processing plants, which may at least partially overcome at least one of the abovementioned disadvantages or provide the consumer with a useful or commercial choice.

[0013] With the foregoing in view, the present invention in one form, resides broadly in a system for remotely monitoring a mine or mineral processing plant, the system including:

a plurality of data sources at a remote mine or mineral processing plant site, the data sources including real-time (or near real time) data sources;

a data assimilation node at the remote mine or mineral processing plant site, configured to assimilate data from the plurality of data sources as it is received; and

a server, located remotely from the remote mine or mineral processing plant site, the server configured to receive the assimilated data at or near real time, and transform the data into measure sets, each measure set relating to a state of the plant at the corresponding period of time, and comprising the data relating to that point of time and historical data for data points in which data does not exist, or only partially exists, for that period of time.

[0014] Advantageously, the system enables the mine or mineral processing plant to be remotely monitored and analysed in or near real time, by in-house and/or third-party personnel. This in turn enables real-time, data-driven decision support to be provided from a remote team to a site team, or directly to the site team. As such, a local operations manager may make better decisions about the site, to increase productivity and to reduce a likelihood of interruptions.

[0015] Preferably, the data includes temporal and non-temporal data. In such case, the temporal data may be stored in a historian associated with the server, and the non-temporal data stored in a relational database associated with the server.

[0016] The data may include one or more of process control data, historian data, distributed control data, delay accounting data, dispatch/fleet data, laboratory information, and operator log sheets or maintenance records.

[0017] Preferably, at least part of the data is captured using sensors associated with mineral processing equipment.

[0018] Preferably, the data assimilation node is a physical device located in a network of the remote mineral processing plant site.

[0019] Preferably, the data assimilation node is configured to transmit data to the server securely using a virtual private network (VPN).

[0020] Preferably, the data assimilation node is configured to transmit data to the server at regular intervals. The data assimilation node buffers the data prior to transmission. The transmission interval of the data assimilation node may be about 1 minute or less.

[0021 ] The measure set may be defined for a period corresponding to the interval of the data assimilation node.

[0022] The measure set may comprise the new data received for that interval, and the historical data for those data points in which new data was not provided (or only partially provided) for that interval.

[0023] The historical data may comprise a duplicate data point, corresponding to the last known data point for that data source. The duplicate data point includes meta-data indicating the age of the data point.

[0024] Preferably, analysis is performed on each measure set independently as it is generated. Such configuration does not require previous measure sets to be analysed, and thus requires a relatively small amount of data to be used, reducing complexity.

[0025] The system may be configured to enable generation of a model, wherein analysis of the measure set is made with reference to the model.

[0026] The system may be configured to send a notification based upon the analysis. The system may be configured to send a notification if the result of the analysis is above or below a threshold. A template may be defined according to a combination of a plurality of variables, and a threshold may be defined according to a relationship of said variables.

[0027] The system may include predictive analytics to determine site efficiency, and to identify potential measures to increase plant efficiency, based upon the measure sets.

[0028] The system may be configured to generate dashboards based upon the measure sets and/or the received data.

[0029] The dashboards may be displayed on large screens in control rooms, to enable remote productivity supervisors to monitor the site(s).

[0030] The dashboards may include dynamic graphing to enable relationships between variables to be evaluated and highlighted.

[0031 ] The dashboards may be interactive. The dashboards may be configurable to display data or variables based upon input from the user.

[0032] The system may be configured to monitor changes in data according to one or more templates or models. A user may generate a template or model defining“normal” operating characteristics, wherein changes are determined according to the template or model.

[0033] The system may be configured to monitor a plurality of sites, using a plurality of data assimilation nodes coupled to the single server.

[0034] The system may include a data mart for each of the plurality of sites to provide clear segregation of sites.

[0035] In another form, the invention resides broadly in a method for remotely monitoring a mineral processing plant, the method including:

receiving data, from a plurality of data sources at a remote mineral processing plant site, the data sources including real-time (or near real time) data sources;

assimilating the data from the plurality of data sources at a data assimilation node at the remote mineral processing plant site as the data is received; and

receiving, at a server, located remotely from the remote mineral processing plant site, the assimilated data at or near real time, and

transforming the received data into measure sets, each measure set relating to a state of the plant at the corresponding period of time, and comprising the data relating to that point of time and historical data for data points in which data does not exist, or only partially exists, for that interval.

[0036] Any of the features described herein can be combined in any combination with any one or more of the other features described herein within the scope of the invention.

[0037] The reference to any prior art in this specification is not, and should not be taken as an acknowledgement or any form of suggestion that the prior art forms part of the common general knowledge.

BRIEF DESCRIPTION OF DRAWINGS

[0001 ] Various embodiments of the invention will be described with reference to the following drawings, in which:

[0002] Figure 1 illustrates a system for monitoring a remote mineral processing site, according to an embodiment of the present invention.

[0003] Figure 2 illustrates a schematic of a system for monitoring a plurality of remote mineral processing sites, according to an embodiment of the present invention.

[0004] Figure 3 illustrates a method of monitoring a plurality of remote mineral processing sites, according to an embodiment of the present invention.

[0005] Figures 4a and 4b schematically illustrate a plurality of data sources, and their processing into measure sets, according to an embodiment of the present invention.

[0006] Figure 5 illustrates a screenshot of an exemplary dashboard, according to an embodiment of the present invention.

[0007] Preferred features, embodiments and variations of the invention may be discerned from the following Detailed Description which provides sufficient information for those skilled in the art to perform the invention. The Detailed Description is not to be regarded as limiting the scope of the preceding Summary of the Invention in any way. DESCRIPTION OF EMBODIMENTS

[0038] Figure 1 illustrates a system 100 for monitoring a remote mineral processing site 105, according to an embodiment of the present invention. The system 100 enables a remote productivity supervisor 1 10, who may, for example, be an experienced plant metallurgist or production superintendent, or any other suitable individual or group of individuals, to obtain an accurate overview of the remote site 105, in real time (or near real time) and in a manner that is easy to understand. This information is then used to assist a local operations manager 1 15 in making better decisions about the site 105, to increase productivity and to reduce a likelihood of interruptions at the site 105.

[0039] The mineral processing site 105 includes mineral processing equipment 120, for processing the minerals. The mineral processing site 105 may be configured to process any type of minerals, and as illustrative, non-limiting examples, may comprise a gold processing site, a coal processing site, or an iron ore processing site.

[0040] A plurality of sensors are associated with the mineral processing equipment 120 and are configured to capture data in (or near) real time relating to the processing equipment 120, and provide same to a data assimilation node (DAN) 125. Similarly, data relating to the processing equipment 120, but not from sensors, such as laboratory data, fleet data or the like, is also provided to the DAN 125. In short, a sufficient number of data points are provided to the DAN 125 to provide an overview of the mineral processing site 105.

[0041 ] The DAN 125 functions as a real-time collector of data, which buffers the data and forwards it to a data centre 130 securely via a virtual private network (VPN). As the data is to be analysed in real time or near real time, the buffer is small (typically around 1 minute or less), and the data is transmitted using real-time services, configured for redundancy and compressed.

[0042] The data collected by the DAN 125 includes both temporal and non-temporal data. In this regard, temporal data is data that is governed by a unique timestamp series/value pair (one-to-one mapping), whereas the non-temporal data is governed by time but in a series of multiple events and multiple value pairs.

[0043] The temporal data is stored in a historical data warehouse of the data centre 130, which in such regard functions in a similar manner to a historian database. The non-temporal data is stored in a proprietary relational database as it doesn’t fit well with the framework of historical temporal data.

[0044] While data may be transmitted from the DAN 125 to the data centre 130 at regular intervals, each of the data points is generally provided at different frequencies. For some data points, new data may be generated each second, whereas for other data points, data may be generated far less often, including as little as once per shift or day. As a result, the data transmitted from the DAN 125 for each interval is not complete, and only includes new data generated for that interval. Furthermore, the amount and type of data in each interval will generally vary over time, according to when the relevant data is generated.

[0045] In order to create manageable data for analysis in (or near) real time, the data is transformed by the data centre 130 into measure sets. Each measure set is generated according to the new data received for that interval, and the historical data for those data points in which new data was not provided (or only partially provided) for that interval. In particular, the data centre determines which data was not (or was only partially) provided and retrieves historical data for those points only. As a result, the measure sets comprise a relatively small amount of data relating to a point of time, and do not include“old” or irrelevant data.

[0046] These measure sets are then deployed to an SQL database as measure tables, which can then be analysed, and from which dashboards and other visualisation may be based.

[0047] In such manner, the entire data of the data centre 130 need not be considered to perform analysis, but instead the most recent measure set, which is relatively small compared to all data of the data centre 130, provides an accurate overview of the site 105 as a whole. The measure sets may be generated at any suitable interval, but in one embodiment, the measure sets are generated at approximately 1 -minute intervals.

[0048] The measure sets may then be analysed by analytics and visualisation modules to identify trends in the data, and to visualise the data in a manner that is easy for the remote productivity supervisor 1 10 to comprehend. The data centre 130 is coupled to a display screen 135, which is configured to display dashboards of the data and results of the analytics to the remote productivity supervisor 1 10.

[0049] The remote productivity supervisor 1 10 may monitor the site 105 remotely using the dashboards to be able to assist the local operations manager 1 15 in making better decisions about the site 105, to increase productivity and to reduce a likelihood of interruptions.

[0050] A similar dashboard, or variation thereof, may also be provided to the local operations manager 1 15 on a portable computing device 140, such as a smartphone. This enables the remote productivity supervisor 1 10 and the local operations manager 1 15 to view the same data, ensuring that the when communicating with each other regarding the equipment 120, they share a common understanding of the state of the site 105. [0051 ] A video conferencing module 145 enables the remote productivity supervisor 1 10 and the local operations manager 1 15 to communicate directly by video conferencing and using a computing device 150. This is particularly useful should problems be identified, as it enables the remote productivity supervisor 1 10 to communicate with the local operations manager 1 15 in a similar manner to as if they were both at the site 105. The video conferencing module 145 may also enable other types of communication and data sharing, including image sharing, screen sharing and the like.

[0052] The video conferencing module 145 may incorporate SSL connection and multi- factored authentication, or any other suitable data security methods.

[0053] The data centre 130, the display screen 135, the computing device 150 and the remote productivity supervisor 1 10 are all located at a remote monitoring site 155, which is located in a different geographical location to the site 105. While only a single site 105 is illustrated in Figure 1 , the system 100 may include multiple sites 105 monitored from a single remote monitoring site 155, as outlined in further detail below.

[0054] Figure 2 illustrates a schematic of a system 200 for monitoring a plurality of remote mineral processing sites 205, according to an embodiment of the present invention. The system 200 is similar or identical to the system 100, and enables one or more remote productivity supervisors to monitor the sites from a single remote monitoring site 210.

[0055] Each remote mineral processing site 205 includes a local network on which one or more computer systems are provided, including a data historian 215, a process control system 220, a lab data system 225, an event data system 230, and a fleet data system 235. The local network includes a data assimilation node (DAN) 240, which is configured to assimilate data from the respective site 205, which may include real-time (or near real time) data acquisition, and historical data. For the sake of clarity, only a small number of systems are illustrated (three per site 205), but the skilled addressee will readily appreciate that a large number of data sources will be used. In fact, a modern site may include thousands or tens of thousands of data sources.

[0056] The data from the DAN 240 comprises temporal data, which is provided to a historian 245 of the remote monitoring site 210, and non-temporal data, which is provided to a non temporal (relational) database 250. The data between the DANs 240 and the historian 245 and non-temporal database 250 is encrypted and sent over a virtual private network (VPN), as outlined above.

[0057] The temporal data from the historian server 245 and the non-temporal data from the non-temporal database 250 is transformed into measure sets for analysis in (or near) real time and stored in data marts of a data warehouse 255. As outlined above, each measure set is generated according to the new data received, and the historical data for those data points in which new data was not provided.

[0058] The data warehouse 255 includes a data mart for each site 205 to allow clear segregation of sites, and the data warehouse 255 is configured for redundancy.

[0059] A data analytics module 260 then analyses the measure sets, as they are generated, to provide insight into the data in the form of predictive analytics and modelling, as outlined below. The data analytics module 260 may include modules and applications incorporating machine learning algorithms.

[0060] One example of such module is a productivity protection module, which utilises a business process modelling engine to create workflows and to track individual signatures for deviations from the workflow. If a deviation is detected, the productivity protection module may be configured to send a notification to a remote productivity supervisor or a local operations manager.

[0061 ] Another example is a predictive analytics module, which is configured to use asset models with predictive analytics to determine site efficiency, and to identify potential measures to increase plant efficiency. Such module may utilise a machine learning service, and one or more third party applications to perform such task, and such process may include creating and training models of various types.

[0062] Finally, the system 200 includes a visualisation module 265, which is configured to generate dashboards for analysis. The dashboards may be displayed on large screens in control rooms, to enable remote productivity supervisors to monitor the sites, or to local operations managers.

[0063] The visualisation module 265, together with insight from the data analytics module 260, transforms and simplifies large data sets into a format which is viewable, digestible and able to be analysed by individuals from diverse disciplines. In this process, it exploits dynamic graphing techniques which clearly highlight the relationships between variables, to thereby tell an insightful and valuable story.

[0064] Figure 3 illustrates a method 300 of monitoring a plurality of remote mineral processing sites 205, according to an embodiment of the present invention. The method 300 may be similar or identical to the method performed by the systems 100 or 200.

[0065] Initially, data is received at a data assimilation node (DAN) 305 for a plurality of data points, including process control data 310, historian data 315, distributed control data 320, delay accounting data 325, dispatch/fleet data 330, and lab information data 335. The data points relate to a mine site, as outlined above, and the DAN 305 is a physical appliance on the site that is geared for life on site and can be easily administered and setup to collect the data.

[0066] The assimilated data is then transmitted, securely, to a temporal data store 340, for the temporal data, and a non-temporal data store 345, for the non-temporal data. This step includes cleansing and categorisation of the data into either temporal data and non-temporal data. In this regard, temporal data refers to data where changes over time or temporal aspects play a central role or are of interest. The non-temporal data is data that, while governed by time, is categorised by other contributing attributes, and encompasses data like delay accounting information, batch information, laboratory assays etc.

[0067] The data of the temporal data store 340 and the non-temporal data store 345 is then transformed into measure sets and stored in a data mart 350 of a data warehouse. Each measure set is generated according to the new data received, and the historical data for those data points in which new data was not provided (or was only provided only to a limited level), and provides an efficient way of getting an overview of a site from a small subset of a large amount of data.

[0068] The data warehouse is a multi-tenanted data store and comprises various data marts for each site.

[0069] A visualisation module 355 is configured to generate dashboards and other visualisation data from both the measure sets of the data mart 350, and the raw data directly from the temporal data store 340 and the non-temporal data store 345, for display to an end user 360. The dashboards may present one or more variables, or a combination of variables, over time, to enable the end user 360 to get an insightful overview of the mine site.

[0070] The visualisation module may generate workbooks which can be organized into individual sheets containing visualizations, to complex dashboards that can embed many sheets. Similarly, the dashboards may be interactive, and be configurable to display data or variables based upon input from the user.

[0071 ] A condition monitoring module 365 is also configured to monitor the temporal data of the temporal data store 340, and generate condition reports based thereon. The condition monitoring module 365 may utilise one or more templates, defined by an operator, together with the data to determine whether operational thresholds have been met. [0072] In one specific embodiment, a template may be defined according to a combination of a plurality of variables, and a threshold may be defined according to a relationship of said variables. If the threshold is exceeded, an alert may issue.

[0073] The condition monitoring module 365 may also enable creation of complex workflows and incorporate algorithms that allow tracking of digital signatures for deviations.

[0074] Furthermore, the condition monitoring module 365 may include an advanced analytical stack, which enables incorporation of additional tools to enable deeper analysis of the data. In this regard, prescriptive analysis, predictive asset analysis and statistical programming software tools may be used to derive and test any additional hypothesis.

[0075] The condition monitoring module 365 is coupled to a notification module 370, which is configured to provide notifications, such as warning messages, to the end user 360, according to an output of the condition monitoring module. As such, the end user 360 is able to be promptly informed of any issues in relation to the site. The warning messages may comprise notifications, emails, SMS messages, or any other form of notification to relevant subscribed parties.

[0076] As outlined above, the data from the data sources is provided at a variety of intervals. As an illustrative example, certain sensor data may be updated once per second, whereas lab data, for example, may be updated only once per shift.

[0077] Figures 4a and 4b schematically illustrates a plurality of data sources 405, and their processing into measure sets, according to an embodiment of the present invention.

[0078] The data sources include a first data source 405a, which is provided at a high frequency, a second data source 405b that is provided at a medium frequency, and third data source 405c that is provided at a low frequency.

[0079] Data from the data sources is sent from a data assimilation node (DAN) periodically. In a first reporting period 410a, data relating to each of the data sources 405a-c is provided. In a second reporting period 410b, data from only the first and second data sources 405a, 405b are provided, and in a third reporting period 410c, data from only the first data source 405a is provided.

[0080] If analysis is performed on the data once per reporting period 410, it is not sufficient to consider only the data for that reporting period. As an illustrative example, the third reporting period 410c does not include any data at all for the second and third data sources 405b, 405c.

[0081 ] If analysis is performed on all reporting periods 410 (i.e. considers the data of the reporting period and all previous reporting periods), the amount of data processed is too large to enable the data to be processed in real time.

[0082] As such, measure sets 415 are created for each reporting period 410, as illustrated in Figure 4b. In particular, each measure set 415 comprises the data for that reporting period 410, and for data sources 405 with limited data in that reporting period, a duplicate data point 420, corresponding to the last known data point for that data source 405. The duplicate data point is associated with meta-data indicating its age.

[0083] As illustrated in Figure 4b, each of the measure sets 415 thus includes data from all of the data sources 405, and thus analysis can be performed on each of the measure sets 415 individually.

[0084] In addition to providing data to each reporting period which was otherwise missing, the use of measure sets increases the quality of the data. As an illustrative example, the second reporting period includes data for the second data source 405b already. Flowever, in the second measure set 415 additional data is provided through the duplicate data point, which enables more accurate interpolation between the data points to be made.

[0085] As outlined above, once the data is received, transformed and analysed, the data is displayed on a dashboard, to enable a remote productivity supervisor, for example, to monitor the site.

[0086] Figure 5 illustrates a screenshot 500 of an exemplary dashboard, according to an embodiment of the present invention.

[0087] The dashboard includes a menu element 505, which enables the user to configure the dashboard, including by selecting a time zone, time ranges for which the dashboard relates (e.g. last 24 hours, or a custom time range), as well as operating plans for the site, such as weekly plan, rate loss, and product time target.

[0088] The dashboard then includes a plurality of critical parameter elements 510, which illustrate data of a plurality of critical parameters, such as total tonnage below plan, total tonnage below rate loss, and productive time (%). The critical parameters may be selected in a template, or be customisable by the user.

[0089] The dashboard further includes a plurality of graphical representations of data, including a concentric pie chart 515, where the inner ring illustrates a percentage of downtime, time below plan, time above plan and below rate loss, and time above rate loss, and the outer ring illustrates current tonnage per hour with respect to plan. The graphical representations include bar charts 520, but may also include any other way of presenting data.

[0090] The dashboard includes an interactive plot 525a, illustrating a plot of first and second variables over time, and a plot 525b of the first and second variables illustrating a relationship between same. The first and second variables are selected by user, and may be selected to test a hypothesis or to identify potential relationships between the data.

[0091 ] The dashboard may be interactive and enable the user to select elements for further information, thereby enabling the user to access more detailed information, or even the raw historical data. As an illustrative example, elements of the dashboard may be selected to either configure the element, or to obtain further information in relation thereto.

[0092] The visualisation module may also provide results of the analysis, and may highlight data, or otherwise identify data based upon the analysis. For example, the dashboard may highlight data that deviates from expected data.

[0093] In some embodiments, the dashboards include filters that allow dynamic visualisation and enhanced interaction. In such case, the filters allow the user to easily experiment with ideas/hypothesis. Furthermore, by providing the freedom to make changes to filters creates transparency and allows exploratory analytics to continue.

[0094] While it is desirable to include a hardware DAN at the site, as outlined above, in addition to, or as an alternative to the DAN describe above, software as a service may be used to connect the site data to the site user’s portable computing devices (e.g. smartphones). As such, site users may monitor the site equipment, receive notifications and create productivity alerts independently of the remote productivity supervisor. In such case, the software may rely on the hardware capabilities of the site, where security is handled via software.

[0095] When such systems and methods are run initially, a historical gap filling task may be initially performed to fill in gaps in the data. As such, a history is initially generated in the system to give context to the data before the subsequent data is captured (and processed) in real time.

[0096] Similarly, before the systems and methods described above are deployed to a site, an initial assessment may be made, which may include face to face engagement between the remote productivity supervisor and one or more local site managers. This enables initial hypotheses of problem areas to be made, and may be used to scope other services that may be offered.

[0097] In particular, the systems and methods described above may be used to provide productivity as a service, where a service provider is assisting the mine site in improving their productivity. The skilled addressee will readily appreciate, however, that the systems and methods may be used internally in an organisation.

[0098] The systems and methods may enable 24/7 real-time (or near real-time) remote monitoring of process performance by a productivity supervisor or other authorised individual, who may be an experienced plant metallurgist or production superintendent. This may be used to stabilise plant performance, and to extract primary value through value preservation.

[0099] Once the plant is stabilised, projects can be undertaken to systematically improve overall output. Services may be deployed via monitoring of actual vs. optimal response to disturbances/events by expert operations and metallurgical personnel. As interventions are undertaken, the data signature of the actual and optimum response may be identified, defined and then captured such that responses to known events may be automated.

[00100] When problems then later exist, expert-augmented investigation may be provided, with rectification and reporting of productivity disturbances.

[00101 ] Advantageously, the systems and methods described above enable remote monitoring of mineral processing plants in real time (or near real time). This enables data-driven decision support to be provided from a remote team to a site team, or directly to the site team. The systems and methods may thus be used as a decision support system to provide effective supervisory optimisation and management of plant productivity.

[00102] Productivity may be improved according to a variety of factors, and in particular a balance between a variety of factors, such as effectiveness (e.g. product output rate), stability (e.g. level of variance or repeatability in generating product), efficiency (e.g. ratio of final product quantity to raw input quantity), resourcefulness (e.g. usage of resources per unit of final product).

[00103] The system may be used to perform multifactor trade-offs, including improving stability by trading off output rate, improving consumables usage by trading off efficiency and output rate, and decreasing capital expenditure at the cost of additional labour and operating expenditure.

[00104] Effectiveness may be improved according to a variety of factors including overall asset utilisation, absolute deviation from ‘target’ (target could be static or dynamic e.g. benchmark model), relative deviation from‘target’, operational time usage under a constraint, and long-term rate of change of any of the above.

[00105] Stability may be improved according to a rolling standard deviation, a statistical control chart (more appropriate for product quality), mean time between stoppage, mean time between feed interruption or any other suitable metric.

[00106] In the present specification and claims (if any), the word ‘comprising’ and its derivatives including‘comprises’ and‘comprise’ include each of the stated integers but does not exclude the inclusion of one or more further integers.

[00107] Reference throughout this specification to‘one embodiment’ or‘an embodiment’ means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, the appearance of the phrases ‘in one embodiment’ or ‘in an embodiment’ in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more combinations.

[00108] In compliance with the statute, the invention has been described in language more or less specific to structural or methodical features. It is to be understood that the invention is not limited to specific features shown or described since the means herein described comprises preferred forms of putting the invention into effect. The invention is, therefore, claimed in any of its forms or modifications within the proper scope of the appended claims (if any) appropriately interpreted by those skilled in the art.

Claims

1. A system for remotely monitoring a mine or mineral processing plant, the system including: a plurality of data sources at a remote mine or mineral processing plant site, the data sources including real-time (or near real time) data sources; a data assimilation node at the remote mine or mineral processing plant site, configured to assimilate data from the plurality of data sources as it is received; and a server, located remotely from the mine or remote mineral processing plant site, the server configured to receive the assimilated data at or near real time, and transform the data into measure sets, each measure set relating to a state of the plant at the corresponding period of time, and comprising the data relating to that point of time and historical data for data points in which data does not exist, or only partially exists, for that period of time.

2. The system of claim 1 , wherein the data includes temporal and non-temporal data.

3. The system of claim 2, wherein the temporal data is stored in a historian associated with the server, and the non-temporal data stored in a relational database associated with the server.

4. The system of claim 1 , wherein the data includes one or more of: process control data, historian data, distributed control data, delay accounting data, dispatch/fleet data, laboratory information, and operator log sheets or maintenance records.

5. The system of claim 1 , wherein at least part of the data is captured using sensors associated with mineral processing equipment.

6. The system of claim 1 , wherein the data assimilation node is a physical device located within a network of the remote mine or mineral processing plant site.

7. The system of claim 1 , wherein the data assimilation node is configured to transmit data to the server securely using a virtual private network (VPN).

8. The system of claim 1 , wherein the data assimilation node is configured to transmit data to the server at regular intervals.

9. The system of claim 8, wherein the data assimilation node buffers the data prior to transmission, and the transmission interval is about 1 minute or less.

10. The system of claim 8, wherein the measure set is defined for a period corresponding to the interval of the data assimilation node.

1 1 . The system of claim 1 , wherein the measure set may comprise new data received for the period of time, and historical data for data points in which new data was not provided (or only partially provided) for the period of time.

12. The system of claim 1 1 , wherein the historical data comprises a duplicate data point, corresponding to the last known data point for that data source.

13. The system of claim 12, wherein the duplicate data point includes meta-data indicating the age of the data point.

14. The system of claim 1 , wherein analysis is performed on each measure set

independently as it is generated.

15. The system of claim 14, further configured to generate a model, wherein analysis of the measure set is performed with reference to the model.

16. The system of claim 14, configured to send a notification based upon the analysis.

17. The system of claim 16, configured to send a notification if the result of the analysis is above or below a threshold, wherein the threshold is defined according to a relationship of a plurality of variables.

18. The system of claim 1 , further including predictive analytics to determine site efficiency, and to identify potential measures to increase plant efficiency, based upon the measure sets.

19. The system of claim 1 , further configured to generate dashboards based upon the measure sets and/or the received data.

20. The system of claim 19, wherein the dashboards are displayed on screens in control rooms, to enable remote productivity supervisors to monitor the site(s).

21 . The system of claim 19, wherein the dashboards include dynamic graphing to enable relationships between variables to be evaluated and highlighted.

22. The system of claim 19, wherein the dashboards are interactive, and are configurable to display data or variables based upon input from the user.

23. The system of claim 1 , configured to monitor changes in data according to one or more templates or models, wherein users may generate a template or model defining operating characteristics, wherein changes are determined according to the template or model.

24. The system of claim 1 , configured to monitor a plurality of sites, using a plurality of data assimilation nodes coupled to the single server.

25. The system of claim 24, including a data mart for each of the plurality of sites to provide clear segregation of sites.

26. A method for remotely monitoring a mine or mineral processing plant, the method including: receiving data, from a plurality of data sources at a remote mine or mineral processing plant site, the data sources including real-time (or near real time) data sources; assimilating the data from the plurality of data sources at a data assimilation node at the remote mine or mineral processing plant site as the data is received; and receiving, at a server, located remotely from the remote mine or mineral processing plant site, the assimilated data at or near real time, and transforming the received data into measure sets, each measure set relating to a state of the plant at the corresponding period of time, and comprising the data relating to that point of time and historical data for data points in which data does not exist, or only partially exists, for that interval.