Realizing the Full Value of Industrial Internet and Data Analytics in LNG Facilities

Transcription

1 Realizing the Full Value of Industrial Internet and Data Analytics in LNG Facilities Jaleel Valappil and David Messersmith Bechtel Oil, Gas and Chemicals Kelly Knight Bechtel Nuclear, Security & Environmental Gastech 2017 Conference, Japan

2 1.0 Introduction LNG industry has grown rapidly in recent years with several new facilities starting production worldwide. A typical LNG facility involves large capital spending to achieve full production. To realize maximum commercial benefit from these capital investments over the lifecycle of the facility, it is important to utilize these existing assets to their full potential. There are several reasons for not realizing the maximum benefit from these LNG asset investments. Some of these include unplanned shutdowns due to unforeseen operational issues, poor maintenance scheduling practices and sub-optimal operation of the facility without maximizing the existing capacity or efficiency. LNG facilities have relied on traditional DCS/APC systems and enterprise performance management platforms to maximize their asset utilization. There is a great potential to further enhance the value from the existing assets using new industrial internet of things (IIoT) and data analytics technologies. The data analytics industry has seen tremendous growth recently (especially in upstream oil and gas industry), and is slowly being adopted by the LNG industry. 1.1 Main features of Industrial Internet The industrial internet is a recent development enabled by the convergence of cloud computing, big data, sensor technologies and cheap computing resources. This technology utilizes very large amounts of realtime data from diverse sources to build model based analytics for various applications. Additional sensors and data previously not collected are utilized to accomplish a variety of objectives. The industrial internet combines the physical plant with the digital information to derive new benefits (Figure 1). It can also be viewed as an integration of Operation Technology (OT - DCS/SCADA etc.) with Information Technology (IT- ERP, Asset Performance Management). This is enabled by the decreasing cost of sensors, computing storage/ infrastructure and innovations in data analytics [1]. The key aspects of industrial internet that differentiates it from past technologies include: a. Cloud architecture The industrial internet utilizes cloud based platforms that can be accessed remotely anywhere and with various hardware. This enables remote facilities to be monitored and optimized from locations worldwide. This is especially useful for floating LNG facilities and remote plants with limited resources onsite. This also enables seamless utilization of applications to several similar LNG plants without additional resource allocation. b. Sensors Addition of sensors to collect data not traditionally collected or utilizing data normally ignored is another key feature of industrial internet. These sensor locations can be selected to fit the objective. Wireless sensors can be implemented for monitoring various equipment parameters. Another prospect is the introduction of new types of sensors that may not have been traditionally used as the applications may justify the added cost. c. Data quantity and variety The industrial internet involves the use of large amounts of data that are collected by various sensors in the plant. This data includes standard DCS/PLC collection tags and other data including from wireless sensors. The traditional DCS databases does not archive the data at very frequent intervals. The industrial internet involves using these data at high frequency for 1

3 various applications. The data collected also includes unstructured data from lab systems and log files. Recent developments in the big data area enables utilization of this variety and volume of data [5]. These features of industrial internet bring new possibilities to the LNG industry. The fact that the data and results can be hosted in cloud means that personnel in various locations can contribute. The large amounts of data, combined with equipment and process knowledge enables new applications in maintenance and optimization. This paper discusses the benefits and ways for the LNG industry to approach this new technology area. Cybersecurity Cybersecurity is critical in industrial internet applications as this involves data flows across the internal and external networks and across multiple domains and organizations. This requires more rigorous security approaches than traditionally used in IT and OT applications. Trustworthy industrial internet systems will require an integrated approach to security covering both information and operational technology areas. Figure 1: Industrial Internet Application Domains (Industrial Internet Consortium) 2.0 Main Applications and Benefits There are several areas where industrial internet can assist the LNG plant owners in realizing the full utilization of their assets. LNG plants are characterized by remote locations and changing optimum conditions due to external factors. Turbomachinery, heat exchange and other process equipment are critical elements in reliable operation of the LNG plant. Sensors are an important component in realizing the value from the investment in industrial internet as they bridge the existing gap between physical and digital domains. These sensors can be driven by various applications that are targeted based on economic value. Examples in LNG plants include vibration sensors for turbomachinery (including refrigeration compressors) and additional temperature sensors in various column tray locations.

4 2.1 Process, Equipment and Control Monitoring One of the benefits of industrial internet is the ability to monitor the LNG plant Key Performance Indicators (KPI) from any location. This is valuable for the operation of the facility as it helps to determine the performance against established baselines. Another application is the monitoring of plant for abnormal operating modes. Abnormal operating scenarios result in lost plant productivity. Identifying these conditions and taking corrective actions early before it results in plant downtime is hence very valuable. One of the key applications of industrial internet is real time monitoring of the performance of the process and equipment using the data collected. The data can be used to build either predictive or unsupervised models to monitor the state of the process and equipment and trigger alarms for operator to take corrective actions. There are several units and equipment in LNG process that can benefit from continuous monitoring for abnormal behavior. These include: a. Amine and Dehydrator units: These units are key in the reliable operation of LNG plants. Any CO 2 or H 2O breakthrough from these units can cause operational problems in downstream liquefaction unit. Amine systems are also susceptible to foaming, which can be detected in advance with proper monitoring. An example of monitoring of an LNG amine unit is given in section 5. b. Heavies removal and fractionation units: The proper performance of this unit is important to ensure heavier components are removed before gas is send to liquefaction. c. Main Heat Exchanger and other exchangers: The MCHE is the heart of a typical LNG facility. The temperature differentials between passes and rate of temperature changes are critical for this equipment. d. PID controllers: The modes and performance of the PID controllers and their tuning can be remotely monitored and diagnosed to ensure proper operation. Depending on a specific LNG plant, there are several other equipment and areas that may be considered for performance monitoring. The cloud architecture enables seamless utilization of these data driven models from one LNG plant to other. This can reduce development time and effort. Virtual Sensors: Virtual sensors provide estimate for process variables that are hard to measure or are measured infrequently. These variables can be determined from other measured variables with a mathematical model fitted to the data. For analyzers with large sampling time, more frequent estimates can be obtained this way. Virtual sensors for process variables including column compositions and LNG properties can be developed for monitoring or control purposes. 2.2 Asset Management - Predictive Maintenance Predictive maintenance is the most important application of industrial internet and data analytics that enable identifying equipment problems beforehand and scheduling maintenance at appropriate time. 3

5 This involves monitoring equipment conditions continuously and predicting when a failure is likely to occur. This also results in greater plant availability. LNG plants utilize turbomachinery to accomplish the liquefaction and transport of natural gas and LNG. These include gas turbines, electric motors, compressors and pumps. Traditional way of performing maintenance is preventive, whereby the maintenance is performed at pre-scheduled intervals. This can result in unneeded maintenance at times. In other times, preventive maintenance may be too late if failures result from events or conditions that occur at any time. Predictive maintenance enables maintenance planning based on actual condition of the equipment (Figure 2). This also avoids the unplanned downtime that can be caused by reactive maintenance. Predictive maintenance is not limited to turbomachinery. Other process equipment, including exchangers and valves can benefit from this [4]. Some of the critical valves can be proactively replaced before failure to minimize unplanned downtime. Heat exchangers can be monitored with additional wireless temperature sensors and cleaned before the fouling starts affecting plant production. Figure 2: Benefits of predictive maintenance 2.3 Plant Operational Optimization This application of industrial internet involves giving operational guidelines to maximize production, energy efficiency or other economic objectives. Currently, most of the LNG plants in operation are equipped with Advanced Process Control (APC) systems, which maximize the capacity or efficiency. These systems are layered on top of DCS and execute every minute or so. Apart from this, some of the operating LNG plants are also be equipped with supervisory or advisory systems that aim to enhance the operation of the plant. Unlike petrochemical and refining plants, Real Time Optimization (RTO) using fundamental steady state models online is rarely used in LNG facilities. There are two important ways in which industrial internet can work to enhance the economics of the LNG facility.

6 - Provide a supervisory layer on top of APC application to further optimize the operation on a less frequent (hourly or daily basis). This is similar to RTO applications, but does not have to be based on rigorous process models. This can be also accomplished using data driven models for the units under consideration. - Capture benefits from optimization of smaller units that are hard to justify as candidates for an expensive APC application. These include efficient ways to run a single equipment or sub-unit that can result in further savings. Some of the typical optimization opportunities in LNG facilities include: a. Determine optimum parameters including MR composition, compressor operating conditions etc. to maximize plant efficiency and production. b. Determine best operating point for refrigeration compressors to enhance efficiency. c. Determine the optimal distribution of load between refrigeration systems to minimize overall energy consumption. d. Energy management and optimization for utilities. It needs to be noted that these are currently supervisory functions that provide recommendations to the operator (Unlike APC systems, which write back to the DCS on a real time basis). 2.4 Business Applications The industrial internet also includes functionality for business processes in the enterprise. This includes ERP, CRM and procedural activities. Demand forecasting, supply chain optimization and other business objectives can be achieved with this technology. 3.0 Leveraging Existing Plant Information and Assets To realize the maximum return from investments this emerging technology area, LNG industry needs to properly utilize the existing plant information and resources. There are several valuable resources in operating facilities and plants under construction that can be beneficial in this regard. 1. Process knowledge and information Process design and operations knowledge is an important asset that can enable realizing the maximum benefit from investment in industrial internet. There are several areas where this is important. 1. This is a key input for the selection of data analytics application and this helps to establish and select the right variables to be included in the data driven models. 2. This is also critical in identifying the opportunities for optimization applications and selecting the right objectives and scope of the same. 5

7 2. Engineering design data LNG plants recently completed or under construction collect the relevant design data in digital format that is used for engineering. The Smartplant Foundation or similar platforms are used to store 3D models, datasheets and P&ID information with digital links among them. These provide a virtual information repository that can be leveraged to benefit the industrial internet development and application. 3. Fundamental simulation models and digital twins LNG facilities utilize steady state and dynamic simulation models during EPC phase to enhance engineering design and ensure safety. In addition, majority of the plants develop Operator Training Simulator (OTS) to assist in training and startup. These models and OTS can be beneficial in maximizing the value derived from industrial internet. In addition, industrial internet facilitates the deployment of digital twins, which are exact replica of a key plant asset that can be used for monitoring, optimization and troubleshooting. The fundamental process models are valuable in various ways in enhancing the data driven models used for industrial internet applications, including: a. Selecting the right model structure: Many of the data driven models (Especially for optimization) need causal models with the correct cause and effect. Identifying this from data can be near impossible in certain cases. Process models can identify the correct causal links and also establish the variables that are significant. This feature selection is an important step in data analytics applications. b. Boosting the data models: There are instances where sufficient data that cover the operating range may not be available from operational data. In these case, simulation runs can augment the data to provide the required range. c. Model Based Optimization: The availability of models and the ability to host these remotely opens up new possibilities to optimize the process remotely. These models can be used for steady state optimization to establish the best operating parameters for the plant. 4. Existing automation infrastructure Existing regulatory control loops and Advanced Process Control (APC) can be leveraged to benefit the industrial internet application [3]. In several LNG facilities, APC applications and supervisory systems are used for control and optimization. Industrial internet can work in tandem with these to establish an additional layer of facility or enterprise wide optimization. 4.0 Benefits of Early Development in EPC There are advantages in developing the industrial internet applications in the engineering and construction (EPC) phase for grassroots facilities (Figure 3).

8 1. Preparing sensors and IT infrastructure First advantage is the capability to prepare the greenfield facility with required sensors and the IT infrastructure. Designing the facility during EPC with these can streamline the industrial internet application without costly retrofits to the infrastructure later. 2. Configuring models based on design and process information Another advantage of development during EPC phase is the ability to prepare the backbone of data analytics application using design data and fundamental models which are available at this stage. This reduces rework and scope of the analytics application during implementation. 3. Starting testing and implementation during startup/commissioning Actual startup and commissioning phase of the plant encounters upset scenarios that results in downtime and delayed commissioning. During this period, the plant operates with different production rates and non-design conditions. The rich data collected can be valuable in training the models. The startup phase provides an opportunity to test and streamline the analytics applications. Figure 3: Development of IIoT during EPC project 5.0 Case Study LNG Plant Amine System Monitoring The application of graphed anomaly detection techniques in determining abnormal operating conditions in LNG process is presented below. This is applied to amine plant in a currently operating LNG facility. There are several advantages to using data driven analytics for monitoring industrial systems. The advantages of using the technique presented here include: a. Reduces plant downtime by identifying operational problems in advance b. Helps to identify root cause of problems quicker c. Can be done without historical data, as it learns online d. Can be set up easily without large investment One of the key steps in the graphed anomaly detection is the clustering of variables for anomaly detection. Statistical methods like k-means and k-medoids can be used for clustering. If design data is available, that can be used to set up clusters based on equipment. Alternately, the clusters can also be developed utilizing the variables selected based on process knowledge. 7

9 5.1 System components and setup The system components utilized for anomaly detection is shown in Figure 4. - Splunk is used to index the machine data coming from the LNG process. Splunk is a widely used big data tool and has the ability to index various types of data including streaming data. - Prelert is used to detect anomalies using machine learning techniques. Prelert is a statistical anomaly detection technique, which develops probability distribution of the variables and updates it as new data comes in. An anomaly score is determined based on the distribution and the incoming real-time data [2]. - Neo4j is the graph database used to store and analyze the design data from Smartplant Instrumentation database and use it for anomaly detection. 5.2 Application to amine system monitoring Application of above tools for detecting anomalies in amine system used in LNG plant is studied. Amine system removes CO 2 and H 2S from natural gas before it is send for liquefaction. The performance of this unit is very important as any carried over CO 2 from amine unit can freeze in the cold temperatures in liquefaction section causing plant downtime and lost production. The historical data from an operating LNG plant was used for this analysis. The data used corresponded to a seven-month period of operation after startup. The relevant tags were identified and corresponding data was collected. In addition, design data related to instrumentation and other items were obtained from Smartplant Instrumentation database to aid this project. The analysis for amine plant data was approached in 3 different ways as explained below: 1. Using all the data for the unit to determine an anomaly score. 2. Using data clustered by key equipment (Amine absorber, amine regenerator etc.). The tags that belong the individual equipment are derived from Smartplant database. 3. Using data clustered by groups as determined from operational experience. Four different groups related to acid gas extraction, acid gas removal, separation performance and amine inventory were identified for this purpose. An example of data driven anomaly detection performance is shown in Figure 5. During the operation, the CO 2 composition was found to increase to 150ppm while the plant feed gas rate was close to the normal rate. This indicated that there was some upset condition that may be causing this. The inlet feed gas CO 2 composition was also found to be steady during this period. During the examination of the data, the column V-1202 operation (Reflux reduction) was found to be responsible for this. The results shown in Figure 5 demonstrates that the anomaly score specifically for the equipment V-1202 did show an increase well before the CO 2 concentration increased. This could have been used to alert the operators about the impending problem.

10 Figure 4: System architecture for anomaly detection Figure 5: Result of anomaly detection. The dotted blue line shows the score. 6.0 References 1. Industrial internet reference architecture, Published by Industrial Internet Consortium (IIC). 2. Veasy, J. and Dodson, S., Anomaly detection in application performance monitoring data, International Journal of machine learning and computing April 2014, Fryer, J., Rethinking automation: A stepwise approach to IIoT, Hydrocarbon Processing, January Johnson, D., IIoT applications enable the process world, InTech Magazine, Mar-Apr Perrons, R.K. and Jensen, J.W., Data as an asset: What the oil and gas sector can learn from other industries about Big Data, Energy Policy, 81(2015)