The Application of Data Analytics in Batch Operations Robert Wojewodka, Technology Manager and Statistician Terry Blevins, Principal Technologist
Presenters Robert Wojewodka Terry Blevins
Introduction Lubrizol Rouen project background and objectives Challenges of applying online analytics Beta project steps Collection of process information Integration of lab and tank property data Instrumentation and control survey Historian collection Model development Training Evaluation Summary More information - references
The Lubrizol Corporation A Premier Specialty Chemical Company Building on our special chemistry, a unique blend of people, processes and products, Lubrizol: Provides innovative technology to global transportation, industrial and consumer markets Pursues our growth vision to become one of the largest and most profitable specialty chemical companies in the world A special chemistry aligned for financial success
Lubrizol s Production Facilities Predominantly batch Some continuous Full spectrum of automation Diversity in control systems Both reaction chemistry and blending Online and off-line measurement systems
Production Challenges Addressing the required batch data structures Better addressing process relationships Characterizing process relationships sooner Identifying abnormal situations/events sooner Better relating process relationships to end process quality and economic parameters Moving process data analytics online
Online Data Analytics Through the use of Principal Component Analysis (PCA) it will be possible to detect abnormal operations resulting from both measured and unmeasured faults. Measured disturbances may be quantified through the application of Hotelling s T2 statistic. Unmeasured disturbances The Q statistic, also known as the Squared Prediction Error (SPE), may be used. Projection to latent structures, also known as partial least squares (PLS) may be used to provide operators with continuous prediction of end-of-batch quality parameters.
Online Data Analytics PCA Fault Detection PLS Quality Parameter Prediction Contribution Plot
We Feel We Have a Solution Lubrizol has expertise and a long-standing use of multivariate data analysis in support of off-line process characterization and process improvement activities. Emerson Process Management established a research project at University of Texas Austin in September 2005 to investigate advanced process analytics. The primary objective of this project is to explore the online application of analytics for prediction and fault detection and identification in batch operations. Tools for PCA/PLS model development and online application have been developed. Through the Lubrizol<>Emerson alliance, we are leveraging these areas of expertise to bring the online analytics to a reality.
Rouen Beta Installation Collaborate on the development of Emerson s tools for on-line prediction of process, quality and economic parameters
Challenges in Applying Online Data Analytics to Batch Processes Process holdups. Tools must account for operator and eventinitiated processing halts and restarts. Access to lab data. Lab results must be available to the online analytic toolset. Variations in feedstock properties associated with each material shipment should be available for use in online analytic tools. Varying operating conditions. The analytic model should account for batch being broken into multiple operations that span multiple units. Concurrent batches. The data collection and analysis toolset and online operation must take into account concurrent batches. Assembly and organization of the data. Efficient tools to access, correctly sequence, and organize a data set to analyze the process and to move the results of that analysis online.
Technical Advancements Two advancements enable batch analysis and online implementation of online analytics. 1. A new approach known as hybrid unfolding offers some significant technical advantages in unfolding batch data for use in model development. 2. A relatively new technique known as dynamic time warping (DTW) is an effective approach for automatically synchronizing batch data using key characteristics of a reference trajectory. However, as with any engineering endeavor, the success of the project depends greatly on the steps taken to apply this analytic technology.
The Steps the Project is Following Our approach at the Rouen plant will be further refined and followed for future applications. Thus, considerable thought is being given to project planning to achieve an installation success. The 7 project steps are: 1. Collection of process information 2. Integration of lab and tank property data 3. Instrumentation and control survey 4. Historian collection 5. Model development 6. Training 7. Evaluation of performance
Beta Project Execution Most of the time required to apply online analytics is associated with collecting process information, instrumentation and control survey, integration of lab data, setup of historian collection, and training. A well-planned project and the use of a multi-discipline team play a key role in the installation success.
Collecting Process Information Important that the team has a good understanding of process, the products produced and the organization of the batch control. Important to have a multi-discipline team Project meetings were conducted at the plant to allow operations to provide input and for the team to become more familiar with the process. This formed the basis of what we refer to as the Inputs Process Outputs data matrix.
Defining Analytic Application Capturing project meeting discussions To address this application, a multidiscipline team was formed that includes the toolset provider, as well as expertise from Lubrizol s plant operations, statistics, MIS/IT, and engineering staff. Data matrix defining parameters to be considered in the project Beta station mapping modules
Beta Installation Beta station is layered on the existing Delta system using OPC. Mapping modules were created in the beta station to allow process and lab data to be collected in a single historian.
Integration of Lab Data Key quality parameters associated with the Rouen plant batch operation are obtained by lab analysis for grab sample. Then, a company typically enters the lab analysis data into its ERP system (SAP software in the case of Lubrizol) The properties analysis for truck shipments are also entered into SAP software. To allow this data to be used in online analytics, an interface was created between the SAP software system and the process control system. The material properties associated with truck shipments are used to calculate the properties of material drawn from storage It is important to characterize both the quality characteristics of incoming raw materials and the quality of end of batch characteristics.
Integrating Lab and Truck Shipment Data Lubrizol and Emerson developed applications to integrate lab data contained in SAP software Online analytic results will also be supplied to SAP software through this Web service interface
Accounting for Feed Tank Properties Storage material properties are calculated using multi-compartment tank model. Storage Tank Design Tank Design 1 Tank Design 2 Tank Design 3 Using the configuration of the mixing and point of entry parameters, the tank behavior can be modeled as fully mixed (CSTR), plug flow or short circuiting.
Tank Properties (Continued) The tank property calculations are implemented as a linked composite block. The truck or lab material properties (max. of 7 per tank), timestamp and transfer quantity are wired as inputs to composite block. Outputs of the composite block reflect the calculated material properties.
Instrumentation and Control Survey A basic assumption in the application of analytics to a batch process is that the process operation is very repeatable. If there are issues associated with the process measurement or control tuning and setup, then these should be addressed before data is collected for model development. Parallel to the initial project meeting, an instrumentation and control survey was conducted for the two batch process areas addressed by the project. Also, changes in loop tuning were made to provide best process performance.
DeltaV Insight for Loop Tuning Beta station modules were created to shadow control loops. DeltaV insight was used to examine loop and get tuning recommendations.
Loop Tuning (Continued) Process loop dynamics and gain were automatically identified based on normal batch operation. Recommended tuning is based on the identified process response.
Historian Collection When the Rouen plant s process control system was originally installed, all process measurements and critical operation parameters associated with the batch control were set up for historian collection in 1-minute samples using data compression. However, for analytic model development, it is desirable to save data in an uncompressed format. This information is collected using 10-second samples and saved in uncompressed format. This allows the data analysis to be done at a finer time resolution and to also further define a more appropriate resolution for future implementation. Analysis of the data will then define if the resolution needs to remain at a fine resolution or if it may be reduced.
Historian Collection (Continued) Emerson developed a special application as part of the project to create the initial data sets needed for model development. DvCH data extraction utility developed to create initial datasets for model development Functionality of this application is being incorporated into the model development tools. The design allows for data files to be exported for use in other offline applications.
Model Development The model development tools are designed to allow the user to easily select and organize from the historian a subset of the data associated with parameters that will be used in model development for a specified operation(s) and product. The tool provides the ability to organize and sequence all of the data into a predetermined data file structure that permits the data analysis. Once a model has been developed, it may be tested by using playback of data not included in model development. Since the typical batch time is measured in days, this playback may be done faster than real time. This allows the model to be quickly evaluated for a number of batches.
Interface for PCA and PLS Model Testing Historian data files may be played back faster than real time. Testing is done with data not used in model development.
Training The plant operator will primarily use the statistics provided by online analytics. Therefore, operator training is a vital part of commissioning this capability. Also, separate training classes on the use of the analytic tool will be conducted for plant engineering and maintenance.
Evaluation During the first three months of the online analytics, operator feedback and data collected on improvements in process operation will be used to evaluate the savings that can be attributed to analytics. It also will be used to obtain valuable input to improve user interfaces, displays, and the terminology being used in the displays. This will allow the project team to further improve the analysis modules to maximize operators and engineers use and understanding.
Business Results Achieved At Lubrizol s Rouen, France plant online analytics are being applied to batch processes for fault detection and prediction of quality parameters. This application in the specialty chemical industry contains many of the batch components commonly found in industry. The analytic toolset Emerson with Lubrizol are collaboratively developing for this installation is specifically designed for batch applications and incorporates many of the latest technologies, such as dynamic time warping and hybrid unfolding.
Summary The use of statistical data analytics will likely cause people to think in entirely new ways and address process improvement and operations with a better understanding of the process. Its use will allow operational personnel to identify and make wellinformed corrections before the end-of-batch, and it will play a major role in ensuring that batches repeatedly hit pre-defined endof-batch targets. Use of this methodology with allow engineers and other operations personnel to gain further insight into the relationships between process variables and their important impact of product quality parameters. It also will provide additional information to help process control engineers pinpoint where process control needs to be improved.