Process Control & Optimization
Overview
Process control and optimization (PCO) is the discipline of adjusting a process to maintain or optimize a specified set of parameters without violating process constraints. The PCO market is being driven by rising demand for energy-efficient production processes, safety and security concerns, and the development of IoT systems that can reliably predict process deviations. Fundamentally, there are three parameters that can be adjusted to affect optimal performance.
- Equipment optimization: The first step is to verify that the existing equipment is being used to its fullest advantage by examining operating data to identify equipment bottlenecks.
- Operating procedures: Operating procedures may vary widely from person-to-person or from shift-to-shift. Automation of the plant can help significantly. But automation will be of no help if the operators take control and run the plant in manual.
- Control optimization: In a typical processing plant, such as a chemical plant or oil refinery, there are hundreds or even thousands of control loops. Each control loop is responsible for controlling one part of the process, such as maintaining a temperature, level, or flow. If the control loop is not properly designed and tuned, the process runs below its optimum. The process will be more expensive to operate, and equipment will wear out prematurely. For each control loop to run optimally, identification of sensor, valve, and tuning problems is important. It has been well documented that over 35% of control loops typically have problems. The process of continuously monitoring and optimizing the entire plant is sometimes called performance supervision.
Applicable Industries
- Transportation
- Equipment & Machinery
- Chemicals
Applicable Functions
- Discrete Manufacturing
- Quality Assurance
Market Size
The advanced process control market is estimated to reach USD 1.4 billion by 2020; growing at a CAGR of 11.79% from 2014 to 2020.
Source: Markets and Markets
Case Studies.
Case Study
The Kellogg Company
Kellogg keeps a close eye on its trade spend, analyzing large volumes of data and running complex simulations to predict which promotional activities will be the most effective. Kellogg needed to decrease the trade spend but its traditional relational database on premises could not keep up with the pace of demand.
Case Study
Detecting and Reducing Cost of Fraud Rings
A leading insurer wanted to reduce its multi-billion dollar expenditures on auto insurance claims, which is its largest annual expense. Currently, the company avoids paying out only about 0.33% of the predicted 10% of fraudulent claims. The insurer uses both manual (60-65%) and automated systems (30-35%) to flag questionable claims. These claims are passed on to the investigative case managers who read adjuster and other member notes, explore the fraud watch list, and search the web. Their existing approach cannot easily discover fraud rings or, more importantly, collusion among other fraud rings. Moreover, the investigative team typically decides in 30 days or less on whether to pay or deny a claim. This is due to heavy caseloads and expectations for a quick turnaround in claim processing in order to keep customers satisfied.
Case Study
GENERAL MOTORS CHOOSES MICROMEDIA’S ALERT TO PRIORITIZE MESSAGES
Opel Rüsselsheim faced difficulties closely monitor on average about 60,000 messages a day which are critical to it's daily operations. This in turn affects the firm's ability to create innovative technologies that strengthen and guarantee the quality of its automobiles