Process Analytics & EHS

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What is PEMS

Credits to the original article by Gregorio Ciarlo Federico Callero and others

Using Predictive Emission Monitoring Systems (PEMS) in Refining Processes

The on-going acquisition of emission data in process industries is regulated by legal requirements so that the release of pollutants to the atmosphere is monitored and controlled. The acquired data is a check on plant emissions to make sure that they do not exceed the threshold limits laid down by the law.

The availability of efficient and reliable emission data acquisition tools is critical for plant owners.

When there is extended failure on the part of a plant in providing emission values, legal authorities may impose a shut down on the plant. Moreover, environmental limitations affect the production of the plant.

The continuous emission monitoring systems (CEMS) used in most plants are hardware-based. The CEMS is made up of analyzers for sampling and identifying the composition of the flue gasses released, and an IT infrastructure for managing, recording and storing the emission values.

An effective alternative to CEMS is the predictive emission monitoring systems (PEMS) that is based on software and accepted by most environmental regulators for monitoring and recording plant emissions. PEMS is a new concept that estimates the concentration of emissions based on advanced mathematical models.

The empirical modeling technique, also known as inferential or data-driven modeling, is considered as the most effective technique for creating precise models for emission estimation.

This technique is based on the ability to derive relevant information from datasets to predict the behavior of pollutant concentrations, which depend on the physical variables that characterize the emissions releasing process. By using Artificial Neural Networks (ANN), the performance and robustness of the model can be balanced in order to provide the reliability and accuracy comparable with that provided by hardware-based emission analyzers.

The details of a successful implementation of neural network technology in a major refining plant located in Southern Europe are provided in this article.

PEMS Rationale and Process Overview

Although PEMS has been accepted as a primary source for monitoring emissions by the US-EPA legislation, the European regulatory authority considers PEMS only as a backup for CEMS. Based on the legal regulations, a top European oil refinery opted to implement PEMS as a backup for the existing emission monitoring infrastructure that used CEMS.

The implementation aimed at increasing the service factor of the hardware analysis system above 97.5% and reducing the number of third party company interventions for monitoring emissions when the hardware analyzers were not in service.

The redundant values of various pollutant components acquired from the fluidic catalytic cracking (FCC) and sulfur recovery units (SRUs) by PEMS. These two units are critical and more complex than the units that are generally considered as the most suitable for PEMS implementations.

This refinery, in particular, used upgraded and modified versions of the FCC and SRU units in order to curb plant emissions and improve the refining capacity.

Sulfur Recover Units

The exhaust gases emitted by the three parallel desulfurization trains are directed to the SRU stack. Each of these streams implements different treatment methods and processing units, but the Claus process implemented downstream is virtually identical in all three trains. There are many bypass valves provided in these trains for selective diversion of the process gas (Figure 1).

Sulfur recovery units layout

Figure 1. Sulfur recovery units layout

The tail gas treatment units (TGTU) in the second and third trains are patented and different. The catalytic incineration stage follows the TGTU stage. The sulfur extraction in the first unit is less efficient because only a thermal incinerator is provided in it.

SRUs receive gasses from various types of refinery treatment and production units. The gases that were fed into the SRUs were of unknown composition and ratio and not fixed over time. A rich stream of gases like H2S, NH3 and CO2 of varying concentration is fed into the three streams of SRUs.

Fluidic Catalytic Cracking

Further treatment of the flue gas from the FCC regenerator was done by the patented absorption process to reduce the amount of SO2 emission. The new FCC unit (Figure 2) is provided with its own stack (FCC-02). The exhaust gas is diverted from the cracking unit either to the absorber or directly to the original stack (FCC-01).

FCC absorption units layout

Figure 2. FCC absorption units layout

The implementation of an effective predictive solution is complicated by the plant layout and processes. The first complication was due to the fluctuating composition of the feeds, which depends on the performance of the upstream units and the hydrocarbons that are processed by the refinery in the beginning. The operator has no control over these fluctuations. Another complication was due to the several operating scenarios of both the units:

In SRUs, the various sub-processes can be operated in numerous configurations, based on the variations in load and maintenance activities for generating different emission levels.

Compliance with environmental constraints is enabled by the SO2 absorption unit. In its active state, up to 50% of the FCC off gases are diverted to the absorber, and later to the FCC-02 stack. In the inactive state of the unit, all the gases are diverted to the FCC-01 stack.

The engineering phase was largely influenced by these operating conditions, and hence, an in-depth analysis of the process behavior and collaboration with the plant personnel was required for comprehensive assessment of the operations of the unit and instrumentation that was available.

PEMS Solution

By working closely with the refinery engineers, the PEMS team was able to clearly define the standard operating conditions that were needed for developing the system. The PEMS application for the SRUs was customized to deliver best performance in the most general scenario, in which the highest sulfur removal efficiency was achieved. In both scenarios, the TGTU2 and TGTU3 operated with a tail gas that was diverted from the first unit to TGTU2.

In the case of cracking units, the software analyzers were designed so an accurate measurement was provided for both stacks, based on the valve open-position value that helps possible shutdown of the SO2 absorber. Creation of a dataset that represented a set of variables that covered the standard operating conditions and described the process dynamics was essential for building an effective model.

The first step was the data-acquisition phase where a baseline was collected of time-stamped and synchronized emissions and process data which was used for creating the model. This step involved the extraction and analysis of six month old data that was stored in the plant emission data acquisition system. The final subset of variables that were used for developing the model was derived by subjecting the initial dataset to several operations:

Removal of outliers and data of ‘bad quality’

Identifying the proper sampling time so that the model overtraining and the loss of important data on process variability are balanced

Statistical analysis conducted by advanced mathematical techniques like principal component analysis help reveal the hidden correlation between the process parameters and emission values

By following the steps listed above, PEMS engineers were able to narrow down on the operating parameters that are suitable to be used as input variables. Since the SRU models involved a large number of units, they required a set of 10-12 input parameters on an average for achieving proper accuracy. The cracking unit required only seven or eight input variables. To identify the right model for reproducing accurate emission values, a number of model structures like linear regressions, neural networks, etc, were generated and their performances were compared.

Based on these comparisons, the engineers chose neural networks as the appropriate model architecture because of its robustness and effectiveness in emission monitoring.

Feed forward and neural network schematic

Figure 3. Feed forward and neural network schematic

Once the off-line validation was completed, software analyzers were installed on a dedicated server that was located on site. Later, an OPC-connection was setup so that the PEMS software engine received the real-time process values from the control system.

Real-time emission estimations were produced based on the parameters processed by the model. Finally, the PEMS system was integrated with the existing data acquisition system (DAS) for making the emission data accessible to plant personnel (Figure 4).

System architecture schematic

Figure 4. System architecture schematic

In scenarios where the traditional instrumentation is not provided data, a strategy was devised to extend the PEMS values to the emission ‘bubble’ limit of the refinery.

Results

The PEMS estimations were validated by comparing the values produced by the system with the values provided by the existing hardware instrumentation for attaining final acceptance by the refinery. The comparison results showed that the predictions provided by the software analyzers were in excellent correlation with the results from the analytical devices.

The predicted SRU flow values plotted against the real-time data obtained from the flowmeter that is mounted on the stack are shown in Figure 5.

PEMS vs CEMS for flue gas flow at SRU stack

Figure 5. PEMS vs CEMS for flue gas flow at SRU stack

According to the figure, the PEMS values are in line and are within the ±5% bandwidth from the physical measurement over the 20-days period reported.

The significance of the PEMS implementation lies in increasing the total availability of the emission monitoring infrastructure on site. In case of CEMS, during the normal maintenance periods, inferential models provided redundant data that helped cover the blank periods.

A daily chart depicting the predicted and measured NO emission values at FCC stack are shown in Figure 6.

PEMS extends emission monitoring availability

Figure 6. PEMS extends emission monitoring availability

As a result of daily automatic recalibration and maintenance activities, there were no emission measurement values from the hardware analyzers during two distinct intervals. This scenario was overcome by PEMS, which provided an alternative measurement and also an overall service factor well above 99% for the emission monitoring infrastructure.

Conclusions

Based on the results, software analyzers are able to provide a highly accurate solution that is a dependable backup to CEMS systems in a highly challenging refinery process. For such applications, any mismatch in the output from the PEMS model and the analytical measurements could trigger early warning of malfunction or drift of the hardware, aiding maintenance action.

Compared to traditional CEMS systems, predictive systems provide well-trained inferential model that enables plant operators to execute off-line simulations of the emission behavior for different operating conditions.

With the help of ‘what-if’ analysis, plant engineers are able to examine the response of emissions to variations in the input variables and  how each operating parameter affects the final emission values. The contribution of the PEMS solution is much more than just a backup for CEMS. It can be implemented as a primary monitoring technology for numerous applications, proving its capability of providing accuracy and performance that can be compared with conventional analyzers.

On the economic front, the PEMS is much more advantageous than conventional analyzers, right from the initial capital to the operating costs. Some of the other advantages include:

Does not require any preventive of periodic maintenance

Near zero power consumption

Does not require any spare parts or consumables, reducing the warehouse necessities

Based on the above benefits and others, implementation of the PEMS serves to reduce the overall life cycle cost by 50%, when compared with conventional analyzers. Even though the predictive systems are able to deliver excellent results, they cannot replace CEMS. The choice of the system depends on the equipment, process layout and operative conditions. An integration of both hardware and software emission monitoring strategies is required for an effective solution that covers the entire range of possible applications.

For instance, PEMS may be a better process for boilers, furnaces or gas turbines, while CEMS would suit civil incinerators or in units where solid fuels are burnt. It is always better to depend on a supplier who exhibits solid capabilities and background in both these technologies in order to receive proper guidance in identifying the appropriate technology for the particular application.

What are PEMS? The acronym PEMS stands for Predictive Emission Monitoring Systems and designates software analyzers able to provide a reliable and real-time estimation of emission properties by means of a model, using process values (temperature, flow, pressure) as input variables.

Are PEMS approved by the environmental legislation? PEMS are widely recognized (e.g. US-EPA, Europe, Middle East) as a source for emission monitoring: depending on the local legislation, they can be used as a primary source of monitoring or as a backup of traditional analyzers.

Which are the basic elements within a PEMS? In order to deliver a complete PEMS application, it is necessary a PC (typically a Server, but also a laptop could easily cover all the requirements) hosting On-Line. The PC must communicate with plant DCS or historian to gather input values needed for emission estimation and to write predicted values. The other crucial component of PEMS installations is Inferential Modeling Platform, the software toolkit that runs the models and provides the estimation of emissions. Depending on the application, a Data Acquisition System (DAS) may be required: for typical applications where PEMS are used as a backup of CEMS, the DAS of the existing Emission Monitoring System (EMS) can be used.

How does PEMS work? The models installed in the PEMS PC have to be fed with values of the main operating parameters identified during the development phase. This is performed typically through a standard OPC connection and the DCS (or plant historian, etc.) as the OPC Server. The same protocol is used to transfer emission estimations from the plant control system or any other data repository used by the Customer. As an option it can communicate also through ODBC protocol.

What are the requirements for PEMS solutions? Plant shall be instrumented (temperature, pressure, flow sensors monitoring the main units involved) and automated (DCS or PLC).

Can PEMS work as an alternative to hardware analyzers? PEMS can be used both as a back-up of traditional hardware analyzers and as the only emission monitoring system, depending on application, local regulation and site constraints.

Can PEMS be sold in place of traditional CEMS solutions? PEMS should be considered as a complement to the traditional CEMS. Depending on application and local constraints, good engineering practice may require installing CEMS, PEMS or a wise combination of the two technologies. More and more complex, large projects will include traditional HW-based CEMS side-by-side with PEMS for selected units. One of the main interests for plant operators, enforced by legislation, is to increase the availability of their EMS: through the adoption of PEMS as a back-up of traditional hardware analyzer, EMS uptime can be increased up to 99.5%. We are pleased to make available its comprehensive portfolio and its extensive experience and competences to design together with the Customer the best possible solution independently from technology or commercial shortages. 3 | Predictive Emission Monitoring Systems – FAQ rev.09/2014 Predictive Emission Monitoring Systems (PEMS) FAQ

What are the typical target units/processes where PEMS can be applied? PEMS are solutions applicable across the industry.

What kind of pollutant emissions can be monitored? PEMS are perfectly suited to estimate all the most common pollutant from process industry: NOx, SO2, CO, CO2, Flue gas flow, etc. For some specific contaminant, as particulate, care should be taken to check the characteristics of the analytical instrumentation.

What are the different phases in a PEMS project? The first step of a PEMS project is represented by the Kick-Off Meeting (KOM) at Customer site in order to gather information about plant operations, available instrumentation and plant control architecture. KOM requires the presence of plant personnel. After KOM, PEMS applications are developed through a series of phases: – Data collection is crucial to obtain the set of data needed for models development: the variables included in the dataset are both relevant process variables (e.g. temperature, pressure and flow) and the emission values; – Design and testing are the steps devoted to off-line model creation and validation: through advanced mathematical techniques included in the key variables and train the models.

Is there any difference in project development if PEMS are designed as a primary source instead of as a back-up of traditional CEMS? The main difference between the two cases is represented by the data collection step: when PEMS are developed as an integration of an existing EMS, emission data are 4 | Predictive Emission Monitoring Systems – FAQ rev.09/2014 Predictive Emission Monitoring Systems (PEMS) FAQ already available and normally stored in the EMS database. In this case, it is sufficient to extract and merge them with process values read from plant historian. On the contrary, if PEMS are designed to be the only monitoring system, it is necessary to arrange a measuring campaign for the collection of emission data; this requires installing a temporary analyzer at plant site for a few days (typically 10-15 days) to obtain the baseline emission data, which will be used to build the mathematical model. Emission data have to be merged with process parameters values to complete the set of information needed for model building.

Is any project step performed at customer site? Only a fraction of the project has to be performed on-site: this is limited to the initial data collection and the final commissioning, while all the other steps can be performed by our technicians and engineers at their office.

How does PEMS compare with HW-based CEMS in terms of price? When applicable PEMS are consistently cheaper than CEMS. The CAPEX saving is only the initial part of the benefit: even larger savings are obtained during process operation because of the limited maintenance activity (typically 1 week per year) and almost no consumables. The life cycle saving, over a 5-year time-horizon can easily exceed 50%.