Takehito Yasui, CHIYODA Corp

MAKING A SMART PLANT WITH AI TECHNOLOGY

Takehito Yasui

Shizuka Ikawa, Akifumi Toki, Chiyoda Corporation, Japan

Recently, AI (Artificial Intelligence) technology has been used in various fields, for example, Big Data analysis, image cognition, self-driving car technology and so on. The process plant field is no exception. We, the EPC contractor community, developed plastic models of the plant thirty years ago, and 3D computer models / databases ten years ago. In the near future a “Digital Twin” of the operating process plant will be made instead. To develop an AI system / “Digital Twin” for a process plant, experienced EPC contractor’s knowledge of the process, equipment characteristics and control systems are indispensable elements. For the remaining indispensable element, Chiyoda started a business collaboration with a top class advanced AI technology company in Japan and combined a state-of-the-art AI technology with our extensive EPC contracting knowledge based on 40+years of experience. The objective of applying AI technology to plant operation is to maximize the client’s profit and minimize Operation cost by 1) running optimized and stable operations, 2) preventing unscheduled plant shutdowns caused by malfunction or abnormal conditions, 3) stabilizing product quality. In this paper, the results of our LNG field trials will be discussed, including AI navigation to recover from unstable operation, AI based suggestions for more efficient operation and early detection of device malfunction. Chiyoda believes that AI technology will complement our existing expertise to make a smart plant that reduces waste and increases the client’s profit. And AI functionality will be incorporated into the “Digital Twin”.

LNG Plant is designed to operate in every possible operation case. Several combination cases for possible feed gas composition, weather conditions, etc. are considered during design phase. In order to guarantee nameplate capacity production for each and every case, most of the cases have some margin other than severe case. To obtain the optimum operation parameters to use remaining margin to earn additional production, it require process study for the new condition. But it is difficult to study on real-time surely because the possible maximum production capacity depends on the input condition to process and atmospheric condition.

Recently the development of AI and digital technology has been remarkable. Even though plant facilities have more complicated relations, the required time for calculating and estimating of the behavior in plant is shorter than before. As a result, this development drives improved profitability; product quality and operating efficiency of plant without compromising safety.

During plant design stage, not only case studies, but also various other studies like HAZOP and so on are done, but previously the judgment was limited to the extent of human cognition and the evaluation was done for the state at individual time point, like over high alarm, or under low alarm.

By utilizing contemporary AI and digital technology, we are able to deal with tremendous amount of data, find large and small interrelationships and causal relationships between different kinds of data from consecutive time series data.

Therefore it becomes possible to find the sign of the abnormal state or trouble by monitoring the multiple relationships between different kinds of data.

Previously operation and maintenance data, as well as various other plant related data, have not been shared across organization and have been handled separately by different disciplines. With the progress of AI and digital technology, it became possible to handle data from different time periods with different sampling frequency on the same platform, and it became possible to find correlations between data which we may have not noticed until now. However, it is still human beings who can interpret data and find and interpret correlations, not current AI.

Fig.1 AI & Big data utilize concept in process plant

Undoubtedly digital technology such as AI, IoT (Internet of Things) etc are crucial for future design and operation of the plant. However, even if a clear correlation is found though big data analysis, that the correlation means causal relationship is not always true.. The plant event is a physical phenomenon, and there is a causal relation as it can be described by a physical model. Only human expert with skill and knowledge in that field can judge that the definitive correlation found by the Big Data analysis is possible from the phenomenon aspect, not AI. Therefore, to utilize AI technology to plant, that the collaborating work with excellent data scientists, AI engineers and plant engineering experts who make such interpretations and evaluations in various fields involved in plant design and troubleshooting for many years is required.

By applying AI and deep learning technology to areas that cannot be digitized conventionally, like the experience or intuition of experienced operators, it becomes possible to make judgments based on measured data and ensure smart operation of the plant. The word “smart” here means “Reduce useless extra margins”.

It is also possible to continuously investigate and present the optimum parameters following the constantly changing operating conditions.

By monitoring subtle operating behaviors hidden in massive operating data, it is possible to predict equipment and process abnormalities, to identify potential issues early or to avoid unplanned stops.

CHIYODA is working on "Smart Plant" concept that utilizes 3D plant platform adopting AI and digital technology. Below, we will introduce some real case examples applicable to LNG plant.

Case 1. Increase Productivity and/or reduce the cost by running optimized and stable operations by LNG Plant AI Optimizer

Production increase by LNG Plant AI Optimizer

As mentioned above, it used to be difficult to calculate the optimum operation parameters continuously as there is an enormous combination of ever-changing factors such as weather and feed gas composition that change constantly in real time. There may also be better operating configuration parameters that have not been discovered so far.

Chiyoda and the partner AI company GRID Inc.'s AI technology made it possible to present the plant’s optimal operating parameters. Focusing on the Cryogenic process and optimizing its operation parameters under given conditions to improve the efficiency and increase LNG production. By reducing the waste of energy we can achieve the “smart operation”.

The LNG Plant AI Optimizer can further increase the LNG production amount and/or increase the production efficiency.

This AI approach does not require any hardware remodeling at all. And development & introduction can be completed within about one year from acquisition of the first customer data.

While investigating the client's plant operations issues, we mainly discussed whether AI could be used for solving the problem. In the Proof of Concept phase we constructed the AI against the hypotheses we had developed through Big Data analysis, derived the correlation between LNG production and operating parameters based on past operations data, and confirmed the existence of increased production margin.

At the first introduction, this system is like a car navigation system. AI calculations are based on the operation data that is input every moment and the system recommends the optimum operation parameters. In actual operation the engineer/operator decides the operation parameters with reference to the AI recommended optimum value. AI does not operate the plant directly, it only supports the operations.

In general, based on our EPC and Debottlenecking experience construction it was thought that in order to increase production by 10% or more it was essential to remodel hardware equipment.

Using this AI approach we can safely and relatively quickly achieve e.g. 2-4 % increase in production capacity without modification of plant equipment.

Case 2. AI forecast to prevent unscheduled plant shutdowns caused by malfunction or abnormal conditions.

Foaming in Acid Gas Removal unit often interferes with plant continued operation.

A phenomenon which has been widely known for a long time is that the amine absorbing solution foams in the absorption tower and the gas-liquid contact with the sour gas is obstructed and gas removal becomes incomplete, so that the operation has to be stopped.

It is known that foaming is caused by heavy HC contained in Feed gas, degradation such as accumulation of HSS of amine solution, and the like. Even though causal relationships are known, they occur suddenly due to nonlinear phenomena, so it is difficult to exactly anticipate the tendency of occurrence even when monitoring operating parameters.

However, the difference in the contribution to the occurrence is influenced by many operation parameters, so the process data should contain a leading indicator of the occurrence rather than sudden occurrence. By analyzing numerical changes not noticeable to humans and AI that can calculate the degree of correlation, which makes it possible to detect symptoms before occurrence of foaming phenomenon and to take preventive measures and avoid stopping operation.

Foaming cannot be viewed from outside the equipment, but when it occurs it can be detected by several sensors; a typical indicator is the sudden rise of the differential pressure in the absorption tower. Analyzing the operation data with AI using these as objective variables, the possibility has come to light that can be forecast before the actual occurrence. [Ref. 1]

Fig.2 Plant failure prediction

We have confirmed that the forecasting the omen of the foaming occurrence in the distillation column is possible, even though in the petroleum refinery.

Attempts to detect the plant operational issues in advance are becoming common in pump abnormal vibration detection and operation monitoring of gas turbines

In order to shorten the rotating equipment downtime and the repair thereof, the operation of the gas turbine is virtually reproduced from the operation data such as the number of start and stop times and the maximum temperature, the creep brittleness and metal fatigue calculations. Digital twin method has been used to calculate the deviation from the initial expected strength of the metal member and calculate the component preliminary life expectancy and the replacement time.

In the process plant, considering the mutual influence of the entire system, including the upstream and downstream process fluid and utilities, it is important to consider not only the individual equipment failure signs but also the trouble signs of a larger system, thus improving operations & maintenance efficiency.

Case 3. Stabilize product quality, and operation itself

Deep reinforcement learning was used in an attempt to use the AI to stabilize the plant process control.

Fig.3 Single stage C3 compressor dynamic simulation model

In this trial, the single stage C3 compressor recycling loop is reproduced on the dynamic simulator, and by disturbing it, it is controlled from the process state intentionally made unstable to let AI learn by trial so as to stabilize the process.

Fig. 4 is to show the concept of reinforcement learning in this trial.

Fig.4 concept of reinforcement learning in this trial

Deep reinforcement learning is a kind of AI machine learning adopted in Alfa-GO, a method by which AI itself searches for a better solution by trial and error. Based on evaluation criteria set by humans, AI searches for behaviors that can have higher evaluation results. Dynamic simulator was used as a substitute because trial and error cannot be done on real plants.

The veteran plant operators obtain the excellent operating know-how from the rich operating experience. AI, also, try a vast number of simulations run with dynamic process simulator and experiences the result of changed the operation parameters. AI have experienced much more simulation cases than the number of the cases that human operator has on actual facilities. Through such experiences, AI learns the action to be taken to shift to the better process state at each state. As a result, with AI learning, it is possible to simulate the results under the operating conditions that human beings cannot produce on actual facilities, so we can expect more efficient operation compared to that of human beings.

The proposition given to AI in this trial is a simple C3 compressor recycling loop, and this closed loop has four PID controllers, LC, PC, Speed Controller, Anti-Surge Controller, since mutual control influences each other, there are cases where a fluctuation is caused from the following state.

Fig.3 Fluctuation caused by mutual control influences in closed system

PID control is a control method that has been widely used in process plants for a long time. Generally, the PID parameter (Proportion, Integral, and Differential) value to be set for each PID controller is not frequently changed. We believe that oscillation is due to the fact that the combination of PID parameters of the four PID controllers does not match the process state, so the PID parameters (4 × 3 total 12 parameters) were assigned to the AI reinforcement learning agent, as an action target and AI learned how to act in order to stabilize the process state. In advanced process control (APC), SP (set point) of control is changed, but in this study, PID parameters were changed.

In the stable state, learning was aimed at converging from transient sate with large fluctuating / to the quickest stable operation after disturbance such as change in operating load and AFC temperature drop due to sudden rain was applied.

AI inputs the 12 PID parameter values of the four controllers at the same time to the dynamic simulator, and then observes the changed operation state for 60 seconds and as a result, it obtains the reward value evaluating the stable / unstable state. AI then re-enters the new PID parameter into the dynamic simulator aiming for the transition to a more optimal state after the next 60 seconds.

In reinforcement learning, it is necessary to evaluate how the process state changed after action, and from this evaluation result, AI judges whether the action taken is correct/ optimal or not. In this case, we devised an expression (rewarding function) that quantifies state evaluation from the following three viewpoints:

a) The difference between the measured value of the PID controller and the control target value is small

b) Measurement value of the PID controller is not oscillating

c) The time required for the measured value of the PID controller to stabilize is short

After several million times of trial & error learning, AI can converge disturbance fluctuations faster than process control with PID parameters set by experienced engineers.

.

It is noteworthy that AI was able to stabilize the entire loop by simultaneously changing 12 PID parameters from the unstable state including mutual interdependence among controllers in the system.

In addition to Feedback control, Feedforwad control of the whole system was performed from the time when disturbance generation was detected.

4) Plant Digital Twin TM

by 3D model

“Digital Twin” is the word for cyber physical model original concept from NASA.

We put forward the concept for the process plant, with 3D platform + Real-Time Monitoring with IoT as “Plant Digital Twin”.

As an example, developing a plant digital twin for a Japanese refinery. Since the target is a refinery built several decades ago, 3D CAD model does not exist. 3D CAD data is not made from the 2D design drawing, but is created from 3D point cloud data obtained by laser scanning the plant and converted into CAD data based on the shape of the equipment piping etc. Maintenance management system, CMMS, inspection management system, corrosion simulation model are linked with 3D plant model. Linking with CFD corrosion simulator enables optimization and advancement of corrosion management and aims to reduce leakage risk.

Fig.6 Integration of plant information, application with 3D platform

There are two options for building a 3D plant model;

① Existing plant designed in two dimensions: 3D plant model creation from 3D laser scan.

In recent years, the cost of creating a 3D plant model from 3D laser scan data (point cloud data) has been greatly reduced due to advanced automation of model conversion.

② New plant and existing 3D designed plant: utilize the 3D CAD model created at design stage.

The above mentioned example is a variety of smart plant that uses the 3D plant model as the base platform. It allows an integrated operation on daily task maintenance and inspection management system. Its potential functions include 1. calculation of the optimum operation condition based on real-time operation data;2. various symptoms of trouble detection; 3. Coordination with CFD to manage corrosion. The functional operation of the cyber physical model of these plants can be called a plant digital twin.

Any additional function can be added to Plant Digital Twin, like adding an application to the smartphone. The important thing is that you can add necessary functional applications as needed.

Fig.7 Plant Digital Twin concept

Summary

As described above, the use of AI and digital technology allows to manage and make improvements in areas that have not been available until now even without hardware remodeling. It is very effective in improving operation efficiency and safety and therefore can drive profitability.

· In addition, the data collected for plant digital twin operation will be useful for the next generation plant designs.

Reference

1. Prediction of foaming and surface tension of lean MDEA solutions with corrosion inhibitor (bis(2-hydroxyethyl)cocoalkylamine) in continuous foam fractionation column

Chemical Engineering Communications 205(7):1-10 · March 2018 with 29 Reads

https://www.tandfonline.com/doi/abs/10.1080/00986445.2017.1423063