Session Type: Paper
Paths(s): Executive Engineer
This session provides specific examples of how specific advanced automation technologies can be applied to drive improved business value. For decades the value of some of these technologies was understood to exist but specifics behind the level of value generation each could provide was scant. These presentations will help automation professionals to justify investments in the technologies that may provide the greatest returns for their businesses.
This paper outlines a model predictive control (MPC) solution for a continuous reactor in order to reduce pH and concentration variability, and consequently reduce costs associated with raw materials usage. MPC is based on constraint, disturbance, controlled and manipulated variables which establish the process control strategy in order to optimize costs and improve process efficiency regarding to process variability, quality control, safety and environmental risks, and raw materials consumption.
For this case study, the plant consisted of a slurry tank in series with a reactor in a recycle loop. In order to maximize production rate using less raw materials, two MPC controllers were created. The first consisted of a single controlled variable (pH), one manipulated variable (income base flow rate), and two disturbances variables (income water flow rate and product outlet flow rate). The second MPC application used a single controlled variable (concentration), one manipulated variable (income water flow rate), and one disturbance variable (slurry density). The modeling process was started by performing the plant runs. Both MPC applications run independently of each other.
This project accomplished the goals, improving the base reactant usage in 3.6%, reducing operating cost with low capital investment. Additionally the MPC applications have demonstrated their importance for reducing by 70% the pH and concentration variability and for maximizing process output.
This case study from a power plant in Florida demonstrates the ability to reduce operating costs through improved process control and the importance of quickly identifying the root cause of a process disturbance. The power plant was able to utilize software based tools and analysis techniques to deliver up to $90,000 in annual natural gas savings and identified the root cause of a possible plant trip. The net effect was improved reliability and increased operating cash flow.
Process variability introduces inefficiencies in the conversion of energy from the fuel source into electricity. Reducing the variability in steam temperature control allows the operator to increase the steam temperature while remaining confident that turbine specification limits will not be exceeded. Higher steam temperatures at the turbine directly translates to increased electrical generation for the same amount of fuel.
Finding the root cause of process disturbances is a tedious and time consuming using traditional analytical methods. This Florida-based power plant looked for root cause with software-based analysis tools including a process interaction map. These tools correctly identified all correlated loops during a known process disturbance and pinpointed which control loop was the root cause.
These methods highlight tools and techniques to analyze real time process variation, as well as process interaction mapping to identify the root cause of a process disturbance. These methods have been able to deliver measurable results faster and more reliably than manual auditing approaches of the past.
IFFCO-Aonla Fertilizer complex consists of two streams of Ammonia Plants and four streams of Urea Plants. Two Gas Turbine Generators (GTGs) cater to the total electrical power requirements of the complex. Additionally there is a back-up connection with the State Grid.
There have been instances of failure of one GTG and consequential tripping of the second GTG as well as the UPPCL feeder overloading, causing a major power outage at the complex and tripping of all major plants. The phenomenon was found attributable to the slow response of the Load Management System (LMS) PLC.
Analysis revealed four critical factors which impacted modifications done to handle this issue. The modification has been done without any investment. Since implementation of the modification, there have been three occasions of trip of one GTG, but no incident of total power failure nor were there any outages of Ammonia Plants.