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Use of advanced process control for the optimisation of the modern cement plant: Juliano Arantes, ABB (Switzerland)

Filmed at Cemtech MEA 2015, 8-11 February, Grand Hyatt Dubai, UAE

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Good morning everybody? Today, I will be talking about the case study from the for the last project in Turkey. And this is related to advanced process control. So our agenda is, we will be talking about the basics of advances process control, we'll be talking about expert optimizer version 8, the case study for [xx] plant, some testimonials, conclusions and I open for questions.

So that is advanced process control? For those that don't know the basic concept is to eliminate, to minimize the influence of the operators, so we go from the manual operation where we all know that we have normal fluctuation of the process and our main goal is to eliminate to minimize the manual fluctuation in the process.

So first of all before we stabilize the process because you cannot optimize something that is not stable. first off you have to make the process as stable as possible. Afterwards we can optimize. So as soon as we have the process stable we can push the system, we can push the to work towards the limit, towards the bottom neck.

We also all know that normally the operators works in this area here with this fluctuation because he does not want to work with the close to the instability, it's more difficult to operate this [xx] if it's more like say the colder side then on the hot side, so this is why the operators, they tend to work on, say in a hot camp, then towards the optimum, and this optimization here, one of the parameters we use to do this kind of optimization is the quality information, so we need quality feedback to optimize the process, does not make any sense to increase production if quality is not good.

So, what are the benefits from stabilizing the process? So, we will have these small but frequent changes, so we will doing small changes all the time instead of Adobe reader thus big changes not very often. We will reduce these deviation from the target, so we will have the process target and we will be working close to those targets, keeping the process stable.

We will have the same operation, the same way to operate the count for the meals 24/7 so there's no variation because we also all know that each operator has their own style, one is more say aggressive or the other one is a bit slower, this is human being. We will have this, let's say we will a spender operation.

If we have more stable operations, that also, we will increase our reliability. So, the maintenance should be decreased. Less blockages, increasing the line there's life time, bricks lifetime, so that leads to better, so all of these. We will also be respecting the process constraints. So we will program this, we will define the limits, these DOS limits are configurable.

The operators or process engineers could easily change those constraints and that will be respected by the program and that cannot be violated. There's no discussion, a limit is a limit, it would follow that. With those benefits, we will have generally speaking, we will have production increase from 3-5%.

Sometimes more sometimes less, but that's the typical numbers. Fuel consumption reduction from 3 to 5 sent as well. Quality variability so we'll improve the stability that wil also improve the quality variability. 10-20% sometimes more sometimes less. And as I mentioned about refectory due to the stability and the working, and the optimum zone we will have also refectory consumption extended into 20% that we all know that it means a lot of money.

So what do we have on the ex pro optimizer, before I go to the key study? So, very short introduction regarding our softer, so that's the exproptimizer where we have applications for accounts, for mails alternative fills stock pile, blending raw mixed preparation, cement blending. Also to say our group we also have the information system part, we also have laboratory automation, and we have integration with SAP or other ERP systems.

We also have on top of [xx] optimize, we have economic process optimization, and this is all connected to the process control, so at the end of the day, ABB is going to help you to close this gap between process control to the ERP system. We have various solutions for that we have this. So as I mentioned we have a stockpile blending, we have raw material blending or raw mill control, we have kiln [xx] cooler control, coal mill, cement mill control, cement blending.

So that covers the overall cement plan regarding optimization. We have more than 100 applications worldwide. So, ABB is the advance process control software platform, so this is ABB's advanced process control software platform, we have more than 15 projects per year. The x-pro miser I mention before, so it's this box here.

If you guys are interested we have outside stand where I have a demo. If you want to have more information about it, you want to know more detail we can go more into detalis afterwards. So mainly it's going to read information from the control system, it will evaluate those information based on the hour in put, based on quality, based on the parameters define, we use the various technologies so virtual sense was Nero Network fuzzy logic, a modern predicted control, it will decide what to do and will send the information back to the control system.

Will send the information back to that traders. We can have on top of that another module is called the economic process optimization that can decide the legal right for example election scheduling regardless checking our selective for electrical energy and so on. So the main tools we have have inside are fuzzy logic that this mainly is going to try to emulate the operator.

The operator's thinking so try to emulate is like to operate to this module. We have also have the nero networks where we try to increase the reliability of certain sensors like the gas analyzer that is often not 100% of the time they are unable, so the reliabilities is quite low in some plans, due to various reasons, so we can minimize this problem using soft answers.

We have predictive control we have linear model, we have none linear model, we have black books model, we have various the whole palette of options. We have the graphical configuration so it's easy to use, it's all drag and drop and so on. So here we have a library where we can select our elements, click, drag and drop.

Configure as all connecting the lines configuring parameters and that this program here will generate, will be connected to the visualization part. And this visualization part it allows me to navigate, go to details and also change parameters. So click the screen it's going to open me another, so what we call the phase plate, where we can see data and we can also do tuning, without being necessarily to come back to program, so it really easy to use.

So, here we have number of bills how does it look like. So, this is the queueing survey page where we have all the information, we have the process values, we have the set points, we have here the main KPIs. So, some light indication if the system is ready, the watchdog is working, if it's online or not. So, from one page you have the overall picture, you know how the working you can navigate from here.

So the calciner survey page is also the same look and feel and then another example about the cooler survey page. Hasanoglan plant case study. So that's a Votorantim plant, it's not coincidence or I don't know maybe it's coincidence because I was born in a plant in a city in Brazil called Votorantim. My dad worked in Votorantim before as well, but I do not own Votorantim just to make it clear.

We have, they downed the business for a long time since 1936, it's one of the largest eight global companies in the industry. They have various plants worldwide. They are present in America, of course in Brazil, in Europe, Asia, Africa and they're a market leader in Brazil. They expanded on 2012, their business so they're now present in Morocco, Turkey, Tunisia, India, China and Spain.

I would like to say thanks for the Hsanoglan plant allowed us to share this case study with you guys. We have this project, this project started end of last year, we successful implemented the first phase. We will conclude this project until the end or until the second quarter where we will round the final test and have the final figures.

So it's a 50 meters long kiln with the calciner commissioned by KHD back on 2009. The current output is around 2, 700 tonnes per day I think. They have mainly coal at 8200 kcal per Kilogram. Have a raw mill 2010 tonnes per hour and coal mill is around 20 tonnes per hour. So what were the main problems or the main challenges we faced before the project.

It's probably similar to what you guys also face or faced before. We had the cleaning requirements. So it's a lot of cleaning the kiln, creating disturbances and the operation, we had done calciner temperature variations especially during the cleaning procedure, and we had the SO2 issues in the kilning lab.

We had also operation shift variability. What were the solutions? How could we help them? So we implemented a cleaning strategy inside expert optimizer. We had the calciner control where could include the other variables to minimize disturbance, we cannot eliminate, but we can minimize the effect.

We also implemented the auto-select for the measurements accorded to reliability of those measurements, and with fax optimizer online as I mentioned before we have 24/7 for the same operation. How do we do that? So mainly we do the same or similar, the same different way but we control what the operator controls.

So we will control fill, klin fill, klin speed, calciner fill, ID fence, great speed and fence law in the kiln. Based on what we've checked the whole sent to temperature, oxygen and CO levels, we will check the parameter, kiln torque NOx. So, whatever signal you have for the burning zone we will also check the underway pressure, clinker quality, we need to have this feedback to then adjust the system, to minimize or to have the process working to give us the required quality.

[xx] trend where we can see the difference, where we have the yield control and the PID control. So comparing the calciner with PID control and comparing manual control. So, they have the ready something and with expert optimizer we could improve a lot of stability and here is one point point where they had a disturbance. So mainly due to the cleaning process, so I cannot avoid the first one but I can minimize.

So, here you see we had the, so let's say the smaller spikes, so is smaller variation and system did not become unstable. So, we can see here graphically good improvement. Here another example regard is the BDT before the SO2 was working towards the limit or beside the saturation. And here we see it's working less saturated so it's working better, it has improved the kiln operation.

Another trend we can see the precall sign and temperature, we can see that with expert optimizer we decrease the variability was not bad. But we see we don't have those spikes and the system is more stable, so we had around 25% reduction on the standard deviation for the temperature. One of the reasons that we can do this kind of control is with expert optimizer, we can add all their variables to our control in the pre-calciner.

For example seed influence, we will have the old tool and seal influence. We can have a tertiary air-temperature influence. So it's much more, say it is more prepared to have the system under control, because you cannot just control fuel to the temperature. One input and one output it's a multi variable problems and we have to consider those inputs into the controller.

Here one, another example of the offline and online where we see that we got the higher output and the count. We also have more ability with SO tool, we implemented the cleaning action, we implemented the SO tool actions. So, the yield will maximize the production with less fuel, of course considering the quality requirements.

One preliminary result, one result are also for the coal mill, we had very good result, we had an improvement around the 13% production and that 2.5% reduction in the specific power consumption. It's a preliminary result but it looks pretty good so far. Here's some feedback we had from the customer.

So, they're very happy with the ABB mainly. So, we say thank to ABB, working live is more easier with the help of expert optimizer that's coming from the operations. The process production is saying fuel consumption is lower when EO is online. Automation said that is adaptable with their automation system, and it's easy to change the parameters according to production unit and the plant manager said that he believes that with expert optimizer they will be able to achieve their targets at the end of the year.

So it suits everybody more less. So, the conclusion, we have we had gave them Votorantim Hasanoglan some performance guarantees before the project. We guaranteed 2% reduction in thermal energy that was their focus, and the coal mill we guaranteed 4% production increase. The projects on going as I said, we will have the final results after the secondary commissioning that should happen before the second quarter of 2015.

Soon as we have the project finalized I'm planning to write, to complete this case study and write a paper that we'll be happy to share with you guys in the future. That was it, more or less from my side on time. If you have any questions you can do it either now or afterwards you can contact me outside.

Thank you very much everybody.

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