WHY & HOW TO DO DIGITALIZATION IN CEMENT INDUSTRY?

Challenges

Challenges & pain points customers are facing in urbanization & infrastructure now-a-days are

·????????Unbalanced market conditions in world—it requires flexible cement production with heavily distributed operations & scalable production capacity.

·????????Low market conditions--it demands highly efficient operations to balance costs.

·????????High market conditions--it requires full capacity usage.

·????????Compliance with environmental policies towards low carbon future- it requires adjustment of processes in direction of minimizing emissions.

·????????Lack of skilled talents--it will be an issue in remote areas but also in central areas where it is more difficult to attract younger generations as a result of mergers and acquisitions globally and locally.

The cement companies need to manage and control complex and distributed operation for ensuring quality.

Solving these challenges from technological perspective is not always trivial as several different aspects must be considered.

We at Sofcon Systems India Pvt. Ltd. believe that emerging Digital Technologies are key to address these challenges as they enable new solution approaches.

To leverage operational efficiency in the cement industry through emerging Digital Technologies, Digitalization is the answer.

It is essential to define an Enterprise Architecture to depict how to tackle these challenges from technological perspective.

A wide range of data sources can be found in the cement production line such as:

·????????Power Data

·????????Process Data

·????????Lab Data

·????????Weighbridge Data and

·????????Dispatch Data

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Apart from above mentioned data sources there are two more types of data sources found in cement plant:

·????????Temporal Data

·????????Relational Data

Every cement plant is having an Automation System either PLC based or DCS based and it always provides the base for further improvement.

Individual IT System takes care of different tasks in the cement plant.

Customer faces challenges for integration of different systems.

Process values are handled by Automation systems to control the production while Dispatch and Laboratory values are usually managed by dedicated IT systems or even spreadsheets by properly integrating those systems under a Plant Data Model which is basically a Historian Database.

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Users like Operators and Site Managers have a quick and easy access to live and historical data all in one place as well as they benefit from the information generated from the combination of distinct data bases.

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This facilitates routine tasks like Plant Analytics & Reporting and Dashboard for Asset Management and Performance Monitoring and at the same time prepares a data repository for Advanced Data Analytics and Process Optimization as required at different locations.

The important place for data in cement plant is the Process Automation System.

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Siemens is one of the major Automation Solution provider for cement industry in the world and they are having a solid base in their automation system and now they are further able to build Digital Solutions on top of it.

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Siemens is really proud of offering Cement specific library to the cement industry in last 40 years of their service to cement industry segment.

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CEMAT is a leading cement process control system developed in 40 years as a result of continuous collaboration between Siemens and cement producers around the world.

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This cooperation has resulted in insertion of all the wishes coming from the operators and process engineers who are running various cement plants using the CEMAT.

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It has helped Siemens to achieve globally the market leadership in control systems.?

Siemens have more than 1000 CEMATIC installations worldwide so far and their success is based on their real process based approach which has resulted in a building comprehensive library with specific features and functions for the cement industry.

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Many topics like

·????????user optimized alarm concepts

·????????advanced information dialogues

·????????easy fault diagnostic

·????????integrated maintenance features and

·????????advanced process control capabilities

are specifically developed for the needs of the segment interests and with CEMAT one can establish a real solid space for further development in the direction of Digitalization and Optimization of the cement industry operations.

Various challenges that the cement customers are facing in relation to lack of skilled personnel and having distributed operations and that is why having key experienced operators to run the plants everywhere is not an easy task.

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Hence it comes to mind, if it is possible to have a central control to run or at least to monitor or to print report through its CEMAT PCS 7 framework.

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CEMAT has already enabled Remote Monitoring and Control of the plants.

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To establish Remote Operation Centre is particularly relevant to optimize the personnel in the company for instance best kiln experts can be assigned to operate more plants from Remote Operation Centre.

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The concept for Central Controlling Design requires to maintain the balance between on-site and off-site activities without compromising safe operations.

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Safety is the key word to manage the tasks for the off-site and on-site personnel to have a safe and secure operation in the plant.

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Cyber security has become a very important topic and also it is more critical when operations are performed remotely.

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Siemens is a leading provider of Industrial Cyber Security software.

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Siemens is actively engaged as one of the founding members of charts of trust initiative or portfolio in Cyber Security arena.

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Its cyber security software is based on defense-in-depth concept in which systems, products and services are integrated to ensure Secure Remote Access at both Application and Network level.

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The success of CEMAT is based on the cooperation together with Siemens customers and the feedbacks that they are giving to Siemens.

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In essence, Siemens is continuously improving its solution.

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An example of a recent development is introduction of Recipe Management functionality which results from a clear challenge to produce different cement types at the same production line and with the short intervals so it is named as Scale Production Control (SPC).

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The idea here is-- it could be different applications like Cement Grinding preparation of the raw mix. Production or Kiln firing.?

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The idea is to define the parameters for a specific cement type or for a specific application so that the operators can immediately start with the production and if they want to change to another cement type for instance with a mouse click they can easily change to another parameter set.

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It will help them or some other producers to manage the challenges coming from the environmental regulations or co2 emission regulations like generating different cement types for instance less clinker and cement and adding different type of additives in grinding preparation.

The importance of Plant Data Model is the basis for Digital Technologies in a CEMATIC plant since many cement producers have many facilities often distributed across cities / countries and even continents.

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The next level of Digitalization is to create an Enterprise Operations having transparency while connecting these Plants to cooperate at management levels in concrete terms.

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Real-time connectivity to multiple cement plants enables the centralized technical and management tasks at corporate level optimizing personal location and minimizing fly-in fly-out efforts to remote areas.

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As an example the creation of an enterprise operations data model allows standardization of concepts indicators and reports in different products sites and facilitate performance comparisons and internal benchmarking integration with Enterprise Resource Planning.

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The enterprise operations data model opens new perspectives for operating cost analysis driven by correlations between financial and technical data.

Moreover, this enterprise operations data model can incorporate queries about Raw mix and concrete stations to generate end-to-end management functionalities such as comprehensive material tracking and overall production planning.

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The semi-auto mission solution has the perfect fit for remote control or basic remote monitoring functionalities for cement plants which are based exclusively on process values however applications that require real-time visualization and analysis of multiple databases of multiple plants are better covered by operations intelligent solutions.

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This typical solution package includes data connectors to assess in real time existing information network data modelling to contextualize motor source data and visualization layer with structure views for different users and drill down capabilities templates for the cement industry are available but one can also generate dashboards and report use cases according to customer specific needs.

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Typical questions addressed by these solutions are:

·????????Which mills had the highest and the lowest operational costs this month?

·????????What's the current benchmark for kiln energy efficiency with the current pace of production? Will I reach my sales targets for the week?

·????????What reasons have been reported that led to the decrease in efficiency in cement line?

·????????What areas equipment are not currently in operation?

So now let's take as an example the demo system where several cement mills from different locations are toured by a central system as can be seen above.

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The live status of these mills on the main screen are depicted along with total production figures which can be easily compared against their individual production targets.

The user can also drill down to a specific mill screen in order to get more detailed information about the current performance as an example of analytical possibilities shown in the right hand side through statistical analysis of power consumption and price of electricity trends the total grinding energy cost profile can be forecasted for this mill and used as basis for increasing energy efficiency.

Lastly performance benchmarking can be carried out through the analysis of historical KPI's of all mills such as specific power consumption and cement production besides generating operational dash boarding and reporting.

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The enterprise data model is also the information backbone to integrate the complete operations value chain.

Here is a practical example coming from a neighbour industry.

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The customer is VALE S A from Brazil, the biggest iron ore producer worldwide similar to cement operations.

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This mining company has long and complex value chain consisting of 38 sites which

include mines process plants and logistic terminals.

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Managing the complete operations was a big challenge and 17 different production management software were previously assisting this task.

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In collaboration with VALE S A, Siemens have replaced all those systems by a single manufacturing execution system especially developed to feed VALE's requirements.

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Compared to all the systems the new MES boosts shorter downtime because of the more modern and robust technology.

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The increased agility and the reduction of unproductive hours are obtained by the higher level of integration with automation systems that provide immediate and reliable data besides requiring less work and be more intuitive to operators, taking less than one year after system was implemented.

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VALE has estimated around 70 million dollars of savings due to this project mainly by posting productivity improvement, asset utilization and optimizing technology costs.

Now let's have a look to the future.

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The focus of the so far introduced solutions is a part of the vertical integration of cement operations and are summarized as below:

·????????Operational Database

1.?????Control Level—Automation and Expert Systems

2.????Field Level—Smart Sensors, IoT Devices

·????????Enterprise Operations Management

1.?????MOM / MES Applications

·????????Corporate Systems

1.?????ERP Applications, executive dashboards

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The ultimate target for this area should be a seamless data flow between operational management and corporate layers ensuring full operations transparency and perfect synchronization between planning and execution activities across the complete cement value chain.

Another aspect of digital transformation is the horizontal integration where complete life cycle of the plant assets such as a Mill or a Kiln ?is managed by a digital database.

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The main challenge here is to establish a relationship between engineering and operational data for which the main precondition is that the engineering project is structured as a database and in an object-oriented manner.

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Typical benefits of this solution would include the increase of efficiency on plant design project, a standardization and improved asset management throughout the complete lifecycle.

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Both vertical and horizontal integrations can be seen as mainly infrastructure topics for data management.

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Siemens added an intelligent layer to their architecture to close the loop of operational improvement which leads to the optimized operation level different emerging digital technologies such as Big Data Analytics, Machine Learning and Artificial Intelligence can be relevant to achieve such improvements and must be properly integrated into the cement ecosystem.

Here Siemens differentiates loops at Plant level and Enterprise level.

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Typical Plant Applications include Advanced Process Control, Online Simulation, Predictive Maintenance and Virtual Commissioning where's Enterprise Applications are related to Value Chain Optimization such as end-to-end material flow simulation.

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Both loops can be closed either within operations and engineering depending on the improvement solution.

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If the optimization leads to a new equipment setting point then the changes should occur at operations level.

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If optimization leads to a new equipment layout or process root then it represents a feed to the engineering solution.

Now let's focus a little bit how to use Data Analytics and Big Data to generate value.

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There are various stages.

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1.??????Descriptive Analytics: In the first stage, the main task is to Analyze the historical data to understand what happened?

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Of course this is not enough—

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2.?????Diagnostic Analytics: The analyzation phase is more important in that sense to understand if something happened why did it happen? so that we can avoid it in the future. This is the second stage.

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3.?????Predictive Analytics: The third stage is more in the direction of predictive analytics.

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???????????This is a further step of analyzing the data and making use of that historical

???????????events to make fault prediction and foresee any anomalies which are coming

??????????up in the right act before losing production etc. and to avoid downturns.

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4.?????Prescriptive Analytics: the last stage is prescriptive analogies so acting also automatically by analyzing the data as it comes to serve learning systems, machine learning and control and optimization of the process.

The approach of Siemens is to combine those technologies and the know-how which exists in Siemens, being the supplier of the automation system since last 40 years in the cement industry, but also the domain know-how, which is a proposition of their customers or

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Cement producers are the best experts who move the problems and understanding the data much better than ever so the idea is getting the data from different processes and sensors, use the domain know-how but also put the

know-how of ?customers, combine it with analytics to achieve

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·????????Improved Performance,

·????????Energy Savings,

·????????Cost Reductions,

·????????Risk Minimization and

·????????Quality Improvement.

It can be said that Siemens’ approach accomplished effective artificial intelligence systems is based on close cooperation between operation experts from customer sites in order to capture domain know-how expertise to train the artificial intelligence algorithms.

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Further it can be concluded that if we get historical data, we analyze it through different type of software's, categorize the data and find also correlations between the different parameters but afterwards the next step comes that we sit together with experts from the production so that we can label the data to understand which is a good operational point, which is a pet equation and with that feedback we can train the artificial intelligence system to achieve either anomaly detection or process optimization.?

Let's give an example from a different field optimization of the customers.

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Siemens has been producing gas turbines since many years however there was no detailed internal expertise available with Siemens team of how even gas turbine is functioning in field.

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What Siemens team did –they used the data and the know-how of the expert from their customers to generate autonomous learning and further optimized the gas turbine operation.

Siemens have more than 30 years of experience producing Gas turbines.

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Siemens have a lot of patrons and technologies and best available turbines but still there is a place for further improvement.

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The control algorithms which had been established by Siemens experts are achieving the perfect result.

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It has taken care of normal production line.

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But when the artificial intelligence algorithms and machine learning are used then immediately one can see the difference –as depicted in the above graph.

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So this is the NO x production of a gas turbine with the standard control algorithms (red line) and if artificial intelligence and machine learning are used then how such a digital solution can improve and it can be seen in the above diagram through a new expression (yellow line).

The same approach is also used in cement industry by Siemens.

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For instance let's talk a little bit about the cement mill.

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It is the most complicated process part in the cement production.

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The operators need to produce maximum potential tonnage of good quality of cement at minimum energy consumption.

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In that sense they control actually four variables:

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·????????Fuel rate

·????????Feed rate

·????????Kiln rotation and

·????????Clinker transmit rate.

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It seems easy but in practice the situation is not so simple.

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There are many challenges that the operators are facing.

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Feed quality is always changing.

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Usage of alternative fuels where calorific values can continuously change so operators have to watch many parameters closely and especially with their experience from historical trends and from the past they should control the Kiln temperature.

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For instance they should understand whether the Kiln is heating up or cooling top as you know the problem is we cannot really foresee or measure the temperature inside the Kiln.

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There are a lot of different type of instruments trying to measure it but of course the dusty environment and the high heat inside the Kiln is bringing a challenge that we cannot really measure the actual temperature and the operator should really foresee if it is heating up or cooling top.

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Best performers achieve in Kiln operation a run rate of 95 or higher and an average operation can reach 90 percent but there are many factors.

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Any failure can generate in red bricks coating, closed broken rings and it causes a downtime.

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Of course then one has to cool down the Kiln, clean it, make the necessary maintenance and sliding it up and warm it up again to the temperature that really is good enough to take again material inside the kiln and of course one can calculate how much production is lost in this period of time so really Kiln operation is a complex art.

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Company needs to have very experienced cement Kiln operators to control that sophisticated process and until now for this problem traditional methods were in use.

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Even expert system cannot be a real solution because all those changing variables like alternative fuels, the cement feed rate, the calorific value of secondary fuels can change anytime because of the content of the material and of course the moisture.

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Now think, what if digital technology which is based on big data analytics and artificial intelligence algorithms can be helpful to solve this problem.

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Here one can see that there are a lot of different variables that one can measure

maybe not correctly, the cement temperature but many other things so the idea is can we make some correlations with artificial intelligence algorithms and foresee the trend of the temperature inside taken so that we can adjust the fuel mix and with the support of these algorithms and technologies it's possible to analyze both different data and make find the correlations among them so we can recognize patterns and

optimize the process inside the key indices.

Let us also talk about other process parts of cement production for instance Intelligent Mill Control Optimization.

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There is a reference from Portland cement plant Rohrdorf in Germany.

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It is a very rigid mill control optimization system by using neural networks and the idea is to use neural networks as a soft sensor to determine the grain size of the production so that one can adjust the varieties and the result is quite good.

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The performance of the mill has been increased nearly up to 8% and the return of the investment was really short.

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Artificial Intelligence can also be applied for automatic anomaly detection in regards to equipment in order to predict potential failure of critical equipment and minimize shutdown.

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It goes beyond the classical condition monitoring of electrical components like drives and motors etc. being extendable to complete assets like a complete mill including the mechanical components in that sense artificial intelligence algorithms enable data analytics into big data as they can identify correlation between different equipment variables and as well as its relationship with failure events.

Here is one more example for the Gearless Mill Drives.

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It is a pilot project running very successfully since several months where different variables are being monitored and then failures of a complete mill along with the mechanical components is predicted.

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Machine Learning, Artificial Intelligence and also cloud technologies are being used to explore the power of big data analytics.

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Customer got following benefits:

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·????????Cost savings

·????????Higher availability of grinding mills

·????????Optimized maintenance plan

·????????Earlier failure detection and

·????????Real time advisory for counter measures.

Conclusion

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Let us summarize both solutions and offerings from Siemens into two different areas—

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·????????The first focus area is Data Integration and Generating Transparency and Benchmarking for the distributed operations of the cement Plant and generating Executive and Plant Dashboards and of course there were chain integration starting from the limestone mill towards the production of the clinker and cement and even transportation and Ready Mix plants so generating Manufacturing Execution Systems.

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·????????The second focus area is the Process Optimization / Advanced Process Control ---one part is Remote and Autonomous Operations & other part is Digital Predictive Services --to make the life of the cement companies and also the operators easier so that their workload is lower.

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The plants are operating in more autonomous operations and it's also easy to monitor remotely and use the existing experienced operators in best possible location and based on Artificial Intelligence and Machine Learning technologies.

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The main focus areas are Process Optimization and Digital Predictive Services so that one can

·????????Prevent down times in the cement plants,

·????????Increase the availability,

·????????Increase the Optimization and Efficiency of the cement plants.

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Now question arises in mind.

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Why should one choose Siemens as trusted partner?

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Answer is simple.

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Siemens understand cement industry and are active for more than 40 years on developing solutions to address cement challenges with proven track record of successful projects implemented in electrification and automation more importantly they understand IT and OT as well as understand how both interact with each other.

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As a leading IT provider worldwide, Siemens can bring into cement solutions relevant capabilities such as big data analytics, machine learning, cloud solutions and their IOT global presence plays an important role as a valued partner.

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Siemens operate globally and covering more than 30% of cement market.

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Moreover in regard to automation systems, Siemens takes innovation seriously.

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Siemens drive innovation with more than 10 percent of company personnel dedicated to R&D and Siemens is currently engaging in different Digitalization initiatives in close cooperation with their customers foreseeing innovative business models and developments to meet and exceed their customer expectations.

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Finally it can be concluded that Sofcon Systems who is the partner of Siemens can help cement producers in their adoption of Digitalization process to embrace Industry 4.0 in order to migrate to 21st Century cement plant.

????????????????????????????????????????????????????????????????????????????????????????????????????????????????By Digital Prabhat

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Resource: World Cement Conference Digitalization (Siemens

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