http://www.yokogawa.com/success/

Success Story

Executive SummarySumitomo Seika Chemicals Company produces superabsorbent polymers, functional chemicals, industrialgases, and other products that are used in theproduction of consumer goods, medical equipment,pharmaceuticals, electronics, and automobiles. Thecompany is well known and highly reputed worldwidefor the high-quality of its products that are producedusing proprietary polymerization and micro-particulation technologies. Super absorbent polymer,the company’s main product, has the advantages ofuniform particle diameter and high fluidity, whichenables the production of highly-comfortable endproducts such as disposable diapers and sanitarynapkins.

The company’s main plant in Himeji, Japan utilizesproprietary polymerization and emulsificationtechnologies to produce functional polymers, latexand emulsions. As a mother plant, the Himeji Workshas a laboratory that researches ways to improveproduct quality and boost performance, and itsresearch results are applied at companymanufacturing facilities around the world.

The Challenges and the SolutionsProcess data analysis solution for improving quality and knowledge acquisition

The Himeji Works manufactures functional chemicalssuch as specialty polymers, which are produced using

a radical polymerization batch process. Sometimesthere were fluctuations in quality in one particularrecipe among various recipes of the specialty polymerproduct. To get to this bottom of this problem,Sumitomo Seika turned to a Yokogawa process dataanalysis solution.

Kazuhisa Uemura, general manager of the HimejiWorks and an executive officer of Sumitomo Seika, onthe decision to go with Yokogawa's solution:

"Our company had already made variousimprovements based on its analysis of data, andthese had achieved certain results. However, throughdiscussion with the plant operators I came tounderstand just how complicated the productionprocess was for this specialty polymer product, andhow important their extensive know-how was toachieving a quality result. It became clear that it wouldtake a lot of hard work to gather together and organizeall the information on this production process. That iswhy I decided to use Yokogawa's process dataanalysis solution to improve product quality and toorganize the tacit knowledge that each person has.

In addition, as our company has been focusing oneliminating barriers between organizations, I expectedthat the members of the project team would activelywork together across departments."

High-quality Production with the Fusion of Process Knowledge and Data Analysis Technology

- Process Data Analysis Using Machine Learning -

Location: Himeji City, Hyogo Prefecture, JapanOrder date: March 2017Completion: March 2018Industry: Chemical (Fine & Specialty Chemical)

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Sumitomo Seika Chemicals Company, Limited

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At the Himeji Works, a project team was assembledthat included people from the production departmentwho were actually working with the process in questionand had a lot of know-how about the process, peoplefrom the development department who wereknowledgeable about chemical engineering, peoplefrom the research laboratory who were responsible fordeveloping new recipes and products, and people fromthe administrative office who had a good overallunderstanding of plant operations. A Yokogawaengineer also joined the project team.

Creation of the observation item list by the project members

In processes involving chemical reactions, there isoften more than one abnormality factor, and thecorrelation between these factors is often complicated.The impact of disturbances such as temperature andimpurities must also be considered. The process inquestion at the Himeji Works was particularly difficult,with a reaction that progressed while adding additiveagents during raising the temperature.

The project team discussed the matter at length in ameeting room and compiled a huge amount ofinformation on the production know-how held by thesite operators, as well as the views of the people fromthe development department and the researchlaboratory. They explored the possible causes ofabnormalities in the entire production process andcategorized them based on the 4Ms (material,machine, huMan, method), then made hypothesesregarding the possible physical causes. Finally, based

on all this, they prepared an observation item list as aguideline for their analysis.

Workflow of the process data analysis

1. Stratification of process data

The Yokogawa engineer started by analyzing not onlyprocess data that had been collected by the plantinformation management system (PIMS) but alsorepresentative values for managing product qualityand operating conditions, and entries in operatorlogbooks.

In plants that employ batch production processes,different products can be produced by changing therecipe. If material from the previous recipe slightlyremains inside the production equipment, this canaffect product quality. And if the time of productiondiffers, there will be differences in productionconditions such as the length of time raw materialsmust be stored and the amount of scaling that occursinside equipment. In addition, as was the case at theHimeji Works, operators sometimes rely on theirprocess know-how to make manual adjustments to theoperating conditions, for example in response toproduction equipment scaling.

When different lots of a recipe have been producedunder varying conditions, it is thus difficult to make anycomparison of the process data that will help to isolatethe cause of a production issue. For that reason, theYokogawa engineer focused his analyses on thoseproduction lots that had the same conditions as thoseunder which abnormal lots were produced.

2. Disturbances

With the analyses of batch processes that involvechemical reactions, impurities are one type ofdisturbance that needs to be considered. The projectteam identified factors and substances such asimpurities that inhibited the reactions. Then, because itwas difficult to verify the impact that these impuritieshave on a reaction by just examining collected processdata, they examined this in a laboratory. The dataobtained in the laboratory was useful for coming upwith solutions.

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Success Story – High-quality Production with the Fusion of Process Knowledge and Data Analysis Technology

Areas of expertise on the analysis project team

Observation item list

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3. Feature extraction

It is also useful to analyze batch processes byextracting characteristic process behaviors, hidden indata, that show the progress of chemical reactions andquantifying them as feature extractions.

In order to extract a feature, it is necessary to use notonly data but also physical quantities such as calorificvalues that are calculated from multiple kinds of data,or to use some kind of conversion processing such asintegration or normalization. The alteration of datathrough conversion processing can help to identifypreviously unnoticed differences in process behavior.The extraction of features is an efficient means foruncovering causes of abnormalities.

Yokogawa's Process Data Analytics tool enables theextraction of features, aids in the understanding of therelationship between extracted features and efficientlyidentifies abnormal factors.

4. Extraction of features at each reaction stage

Even if features can be extracted, it is not possible toidentify the true cause of an abnormality by justanalyzing the relationship between the quality valuesand the extracted feature. In order to do this, it isnecessary to analyze the data as the reactionprogresses. The project team divided the reactionphase into early, middle, and latter stages, identifiedthe extracted features for each stage, and analyzedthe relationship between product quality and theindividual extracted features.

To illustrate, the following analysis was carried out:

The project team noticed that power consumption wentup as the result of an increase in the agitation load asthe reaction progressed and polymers were depositedin the polymerization tank. They defined the point intime when power consumption peaked as theextracted feature for the latter reaction stage. A strongcorrelation was identified between the extractedfeature, the quality value, and the total amount ofadditives. However, to keep product quality high,operators needed to take action before the powerconsumption peaked. The project team continued tolook for other features throughout the production

process and found out that the heating valuegenerated after the reaction started in the middlereaction stage had a decisive influence on the latterstage's feature (power consumption for agitation).They reached the conclusion that the quality valuecould be controlled by adjusting the additive amountdepending on the level of the heating value that wasgenerated right after the start of the reaction.

To solve problems in this fashion, it is of coursenecessary to discover correlations between qualityvalues and extracted features, and relationshipsbetween extracted features, using data. Furthermore,it is also important to interpret the results of theseanalyses by examining them from a broader viewpointthat takes into consideration factors such asproduction, chemical reactions, chemical engineering,and the 4Ms. The bringing together of processknowledge and analytical technology is essential.

5. Verification of feature extractions by machine learning

The changes in the extracted features at each stage ofthe reaction more or less impact product quality.Machine learning makes it possible to identify thedominant extracted feature and to verify the certaintyof this conclusion. Machine learning is also able toreduce the risks of implementing an action plan forproduct quality stabilization.

The project team verified the certainty of the extractedfeatures using stepwise regression, a statisticalanalysis method. Three explanatory variables thatcontribute to the reaction, including the heating valuegenerated right after the start of reaction, wereselected from the extracted features. According to theestimated result, the quality value could be reliablyestimated by using these explanatory variables. Theproject team concluded that the quality value could becorrectly controlled by controlling the three extractedfeatures.

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Success Story – High-quality Production with the Fusion of Process Knowledge and Data Analysis Technology

Process Data Analytics screen

Modeling result by stepwise regression

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Results

After the factors that had caused fluctuations inproduct quality were identified, the project teamdeveloped new operating procedures that includedadjusting the additive amount depending on theextracted feature showing the progress of the reaction.They also designed a display that compares thecurrent extracted features with past lots. The systemenables site operators to carry out their duties whilemonitoring the progress of a reaction. It is expectedthat the new operating procedures for controlling thefluctuations in quality with the quality value will reducecosts by several million yen per year.

In this analysis project, the team created a list of itemsto observe, made hypotheses, and verified thehypotheses with data. In addition, new observationitems were sometimes discovered during the analysisand verification of data. Through these tasks, theywere able to gain an overall understanding of plantissues, discover the true root causes of abnormalities,and devise countermeasures. There were alsosecondary results such as finding new challenges thatneeded to be addressed to improve product quality.

The revision of the list of observation items was madepossible by the combined knowledge of all the projectmembers, and this is an asset that has been proved bythe data and can be used as a technical standard toadvance technological innovation at the Himeji Works.The Himeji Works aims to further improve productquality and enhance its competitiveness by promotingthe use of data analysis and the fusion of processknowledge and data analysis technology.

Customer SatisfactionMr. Uemura:

"Initially, I thought that it would be okay if we could justcollect the tacit knowledge each person has by usingYokogawa's process data analysis solution. However,we were able to find certain results in product qualityimprovement. Furthermore, we were able to makeimprovements to operations, improve capabilities inthe field, and strengthen internal cooperation acrossdepartments. Above all, creation of the observationitem list aided in the conversion of tacit knowledge toexplicit knowledge. This exceeded my expectation andI am very satisfied. In order to continue providing highquality chemical products to the market, we would liketo make further improvements by utilizing theknowledge and experience gained through thisanalysis project."

The project members:

Q. What was your impression when you were selected for the project?

“It was absolutely necessary to improve this recipe, soI thought that we should go ahead with the processdata analysis."

"Since I had no experience with data analysis, I had noidea what I should do at the beginning. I knew ourplant had already tried conventional approaches tomake improvements, but that they had not yet attainedall the desired improvements with this recipe. So myexpectations for Yokogawa's analysis were high."

"It was an honor to be selected. I am engaged inaccounting work in the administrative office and amnot involved in production, so I was wondering how Iwould be able to contribute when I started work on thisproject."

Q. What did you gain from the project?

"We could find new perspectives that differ fromconventional knowledge. And the conventionalknowledge that we had was also proved by the data."

"I enjoyed free and active discussion with people fromother departments. In the past, we vaguely knewbased on our experience what properties a productwould have when produced under certain conditions.But after finishing the analysis project, I think I cannow see what is going on in the process. I would liketo utilize the analytical method with other productrecipes."

"I knew that Yokogawa would use difficult analyticalmethods, but they listened to us carefully in order tounderstand our operations. Yokogawa did very steadywork, and I recognized that organizing huge amount ofdata is fundamentally important if you wish to makesmooth progress with tasks."

"We were often bound by rule of thumb, and this tacitknowledge in each department was closed off toothers. We successfully organized and visualized thatinformation through this project. It was also very goodfrom the human resources viewpoint to shareknowledge and ideas across departments."

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Success Story – High-quality Production with the Fusion of Process Knowledge and Data Analysis Technology

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Q. Did anything change as a result of your experience with the project?

"My handling of accounting data changed once Irealized how valuable it was. I now consider how toextract knowledge from a lot of data, and so I have adifferent point of view on how I will utilize data."

"I now use spreadsheet software more frequently. Ionce thought that there was no correlation betweenthe data, but now know that it is possible by means ofcareful analysis using stratification and other functionsto discover relationships between the data."

"I began to ask site operators to give hard datawhenever possible to explain what was going on in theplant. Operators who previously only used descriptorslike “hot” or “cold” began to give more concreteexplanations backed by numeric data."

"Unproductive tasks were eliminated, dramaticallyreducing operator workload."

Q. Do you want to try the analysis again?

"We have already analyzed another recipe.Opportunities for discussion with people in otherdepartments have also increased."

"With the observation item list as a reference, we havebeen testing various things using laboratory and actualproduction equipment. Thanks to the list, we are ableto do analysis smoothly and without bias."

"The research laboratory has developed a difficult newrecipe again! That is also complicated and difficult tocomplete, but utilizing the knowledge that we obtainedthrough this analysis project we will be able tocontinue to produce high-quality products."

"Even though the work to retrieve production data atthe site was troublesome, I enjoyed the project. It wasfun!"

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Success Story – High-quality Production with the Fusion of Process Knowledge and Data Analysis Technology

Back row (from left)Mr. Takenaka (Administrative Office)Mr. Matsuura (Functional Polymers Section)Mr. Maki (Yokogawa)Mr. Yamaguchi (Functional Polymers Section, project leader)Mr. Murakami (Functional Chemicals Research Laboratory)

Front row (from left)Mr. Endo (Technical Office)Mr. Masui (Functional Polymers Section)Mr. Tatsumi (Technical Office)

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Tomotaka Maki, Yokogawa engineer:

"With the analysis solution for Sumitomo Seika, I wasin charge of everything from the initial sales promotionphase to the completion of the project, and carried outthe actual analysis work.

Process data analysis does not always work well if thecustomer lacks the strong desire to improve and failsto exercise leadership. It’s hard to know how this willbe until we start in on a project. Mr. Uemura spent a lotof time right from the beginning trying to understandYokogawa's activity, and assigned knowledgeablepersonnel from different departments to work on theproject. I truly appreciated his good cooperation.

I hope that the culture of analysis takes hold atSumitomo Seika. The fusion of process knowledgeand data analysis technology can contribute toimprovement of quality and operational efficiency. Ihope the project members will continue to activelyinnovate. I also trust that they found the data analysisexperience rewarding and that they feel a sense ofachievement in having made improvements."

For more Information and Contact

Process Data Analytics

YOKOGAWA ELECTRIC CORPORATION

World Headquarters

9-32, Nakacho 2-chome, Musashino-shi, Tokyo

180-8750, Japan

www.yokogawa.com/

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Success Story – High-quality Production with the Fusion of Process Knowledge and Data Analysis Technology