Asset Data Management and Integration Support - A Physiological Project Need

    An article published in Chemical Engineering World, July 2016 Issue by Mayuresh Mokal and Dr. Pratap Nair

Increasing levels of global competition and rapidly changing market conditions behooves chemical manufacturers to continually look at ways to maximize their asset utilization and effectiveness, either through operational improvisations to increase throughput, yield and reduce costs or by expending additional capital. Resulting engineering projects tend to have tight deadlines and availability of “right” engineering information from the plant has always been a bottleneck in most petrochemical, chemicals or refinery complex projects.

Projects in petrochemical, chemical or refinery manufacturing, whether large or small, inevitably require as well as generate enormous and complex sets of information. Effectively managing this bulk of information to ensure its timely availability and accuracy is an important managerial task. Poor quality of information or missing information can lead to project delays and suboptimal or even uneconomical decisions.

Even when the information is available, it is not uncommon to have the following challenges act as major hurdles during project execution:

• ·      Data available at multiple locations
• ·      Availability of data in unstructured or non-standard format, making it difficult to use.  For example, scanned copies of P&IDs, which are illegible.
• ·      Inconsistency of data within same information set.  For example, the design datasheet may indicate a tube size of 1/4 inch whereas general arrangement drawings may show it as 3/8 inch.
• ·       Missing as-built data
• ·       Multiple versions of the same engineering documents without any proper indication of the final version
• ·       Multiple sources of the engineering documents, such as line list, cable schedules, etc.

These discrepancies results in lack of confidence in the data to use and the one cannot rely on the set of information available resulting in extra effort and hence project delays or if the extra effort is not taken, it could result in suboptimal or uneconomical decisions, operational issues or even the project failure.

In old plants, data (vendor or contractor documents and drawings) is available and stored in the form of hard copies, making mining of data a time consuming task as well as putting a question mark on reliability, consistency and completeness of the data. With advancement in technology and introduction of sophisticated software and applications, generation and management of information may generate compatibility issues between client and contractor, if the system is not unified into an integrated database. It is challenging to collect data from different systems and maintain consistency.

"A survey conducted by Gartner indicates that an average organization loses $8.2 million annually on account of poor data quality. Further, of the 140 companies surveyed, 22% estimated their annual losses resulting from bad data at $20 million. Four percent put that figure as high as $100 million."

Data needs typically arise on projects done at various stages of the lifecycle of a chemical plant:   

•        Design
•        Engineering
•        Commissioning
•        Operations
•        Expansions
•        Modifications
•        Statutory or Regulatory Requirements

Both engineering design projects and operational improvement analytics projects are crucially dependent upon accurate and timely information availability as well as the ability to use this information effectively. At the same time unnecessary information presented to managers can result in confusion and decision-making paralysis. 

Having the right information available at the right time, in the right form and the right amount is key to evaluating and making decisions on Capex and Opex projects to reduce production costs, increase capacity or throughput, enhancing safety measures or even setting up a new or revamped plant.  There is a significant hidden cost on both ongoing operations as well as projects, where the systems to store and retrieve quality data which can be readily converted to usable information and knowledge have not been properly established.

There have been examples of companies who have acquired older or underperforming assets at an “attractive” price, realizing later that there is a price-tag to be associated with the existence of well maintained data and information structures, which may have been overlooked at the time of due diligence and price negotiation.  Missing data or poor quality data can increase cost and timelines on projects, whether for    efficiency improvement, compliance, revamps or expansions. In case of the examples of companies mentioned above, the document control room in the plants comprised of several files of engineering data and documents without any proper identification of final or the as-built version, making it difficult for a contractor appointed to perform revamp work, increasing costs and timelines. In this case, there were occasions where it was observed that certain engineering documents which were critical input documents for the project, were not even in a legible format, which translated into delayed work, resulting delay in plant start-up and finally loss of revenue.

Ingenero has supported some of their Process Manufacturing clients address such shortfalls in this area, with their Asset Data Management and Integration Support Services (ADMISS).  

ADMISS provides a scientific and proven approach to create a centralized repository with structured and consistent data along with as-built information plugged-in which becomes easy for use or retrieval.  Additional levels of analytics or Business Intelligence interfaces are typically added on, once the data foundation is set, which can then be accessed from remote locations using the Industrial Internet of Things (I-IoT) concepts, for operational improvement use or engineering project inputs.

ADMISS entails the involvement of subject matter expertise and technology to manage asset data and convert it into decision quality knowledge.  The involvement of multi-disciplinary subject matter experts is extremely important in this process, absence of which will create a dilemma in the selection of the right set of information, thereby hampering the ability to make optimal or sometimes even the correct decision.

The ADMISS Solution consists of some key steps, starting with data collection through consolidation of necessary data/information into a readily accessible database, which helps convert raw data into knowledge, enabling correct decision making.

The key steps to the ADMISS solution are

Data Collection is the most critical portion of the data integration program wherein the domain experts screen the right information before it lands up in user’s hand thereby saving on precious time later. Experts have observed that improper screening of data has led to project loss worth millions of dollars hence involvement of subject matter expert (SME) in the data collection team is of utmost importance. The process involves field walk-down by data collection team led by SME to prevent the hurdles that can be faced during later stages.

\The main purpose is to extract information from collected data and transform it into an understandable structure for further usage                                                                                                                                                  

Data Mining is carried out using advanced analytics and other state-of-art tools and techniques such as artificial intelligence or statistical programs, from a pool of collected data.

Reconciliation becomes a crucial part of the step wherein information may be available in bits and pieces as well as at times from multiple sources such as engineering drawing, operations data, etc. but not consistent with each other. It becomes essential to reconcile this information to avoid duplication, gaps and inconsistencies.For example, Piping Isometrics may indicate existing pipeline from reactor to column of size 8 inch but after walk-down it is identified to be 10 inch.

This is a common scenario in most plants undergoing frequent modifications and where these details are not updated in the engineering documents.  This acts as a major hurdle in carrying out further studies.                                                                                               

Validation of Collected Data needs to be carried out in conjunction with reconciliation and requires presence of domain experts for cross checking calculations and for verifications. The outcome will result in omission of obsolete information.

Data Consolidation enables the user to have a single repository access to information instead of indexing it across multiple locations.

Benefits of the ADMISS solution:

·         Single repository with ease access, transfer of quality information

·         Structured and consistent information

·         Assurance of data with improved decision ability

·   Shorter times to initiate and execute engineering for expansion/revamp and operations improvement projects

To know more details contact at mmokal@ingenero.com or visit us at www.ingenero.com