Machine learning for monitoring and control of NGL recovery plants

In this contribution, the monitoring and control problem of the natural gas liquids (NGL) extraction process is addressed by exploiting a data-driven approach. The cold residue reflux (CRR) process scheme is considered and simulated by using the process simulator Aspen HYSYS®, with the main targets of the achievement of 84% ethane recovery and low levels of methane impurity at the bottom of the demethanizer column. The respect of product quality is obtained by designing a proper control strategy that uses a data-driven approach based on a neural network to estimate the unmeasured outputs. The performance of the controlled system is assessed by simulating the process under various input conditions evaluating different control structures such as direct control and cascade control of the temperature in the column

Control of a natural gas liquid recovery plant in a GSP unit under feed and composition disturbances

Recent technological improvements have driven the rapid increase in natural gas production from unconventional reservoirs. The heaviest hydrocarbon fraction of this fossil fuel, the so-called natural gas liquids (NGL), have greater economic interest justifying the attention on its separation process from the raw gas. Various process schemes have been developed and studied for the NGL recovery, including the conventional, cold residue recycle (CRR), and the gas subcooled process (GSP). This study aims to assess different control strategies for a GSP unit and determine the most appropriate and effective process control scheme. For this, the dynamic responses for each control scheme are evaluated by changing feed flow rate and composition. The main targets are the achievement of 84% ethane recovery and low levels of methane impurity at the bottom of the demethanizer column. Due to the high cost of composition analyzers and the high delays introduced by composition controllers under the presence of flow disturbances, the control goals are reached by the knowledge of on-line temperature measurements. This is done by considering different temperature control structures such as the direct temperature control and cascade control, plus a pressure compensator. The results are compared, in presence of composition disturbances, with the action of a hybrid cascade control that uses in-line delayed concentration measurements to update the controller reference at each sampling period. Here, the hybrid and the simple cascade controls show the best control performance

A Demethanizer column Digital twin with non-conventional LSTM neural networks arrangement

This work aims to develop a digital twin for a demethanizer column and provide a useful tool for monitoring and quality control of the NGL recovery process. For this purpose, a digital data-driven model was proposed to mimic real dynamics of a cold residue reflux (CRR) unit through the incorporation of physical knowledge. A non-conventional LSTM network arrangement was developed considering training test and validation data sets generated by the process simulator Aspen HYSYS®. This simulation model was built by considering realistic measurement noises to mimic the actual measures in a real plant. The obtained surrogate model was evaluated considering its ability to recreate the operation of the actual distillation column, estimating the temperature and composition transient profiles of the bottom column product and of every stage of the column. Overall, the model developed with the proposed LSTM network arrangement proves capable of successfully reconstructing the actual profiles of all the considered variables

Control Strategies for Natural Gas Liquids Recovery Plants

Nowadays the improvements on the extraction methods of natural gas has increased the availability of natural gas liquids (NGL), which represent a valuable source of energy and industrial feedstock. Several process schemes have been developed in the past for an economical, safe and efficient recovery of these components including the conventional, cold residue recycle (CRR), and gas subcooled process (GSP), each comprising a range of competing advantages. In the present work, the control problem of the NGL extraction in a CRR process scheme is addressed aiming to achieve a recovery of at least 84% of ethane while maintaining low the level of methane impurity in the bottom of the demethanizer. Considering the high cost of composition analyzers, different temperature control structures are assessed. The temperature direct control and cascade control are proposed to improve the rejection of the disturbances. The operability of these NGL recovery technologies is evaluated under typical disturbances

Performance assessment of control strategies with application to NGL separation units

In this contribution, the problem of NGL separation control is addressed by dealing with the most common process schemes. The main goal is to achieve a specified ethane recovery as well as maintaining certain levels of methane impurity in the demethanizer column. An indirect control of composition through the temperature control in the column is proposed. A cascade arrangement between the column temperature control and the controller that maintains a constant ratio of boil-up to column bottom product is proposed for the improvement of methane impurity levels. Additionally, an “inferential” control approach based on Antoine's law is formulated and tested to enhance the ethane recovery control. The performance indexes calculated for ethane recovery and methane impurity show the superiority of the proposed control structure in each NGL separation process scheme. When the feed flowrate is reduced by 10%, the proposed control strategy allows a lower deviation from the target and a smaller offset with a reduction of 73.7% for ethane recovery and 72.7% for the methane concentration in the conventional process, 86.6% for ethane recovery and 96.4% for methane concentration in the GSP, and 97.1% for ethane recovery and 91.1% for methane concentration in the CRR process. In case of sinusoidal variations of inlet flowrate, the integral square error is reduced by 99.33% for methane bottom concentration in the GSP process scheme, while ethane recovery shows a reduction of 82.69% in the CRR scheme

Effect of the demethanizer improved control strategy on the separation train for the NGL separation process

In recent years the attention on natural gas production and utilization is growing due to different fundamental aspects. First, the availability of natural gas has increased thanks to technological improvements in the extraction techniques that have made possible the production from unconventional reservoirs. Second, the interest in clean energy is growing, aiming to reduce CO2 emission and thus global warming. Natural gas is a cleaner fossil fuel compared with other traditional energy sources such as oil and coal. Another reason that drives the attention on this fossil fuel is the increasing economic interest of recovering the heavier hydrocarbon fractions contained in it. The fractionation of natural gas liquids (NGL) is an energy-demanding process, often conducted with a separation train that includes cryogenic distillation columns. This work is intended to show how to achieve an energy-efficient recovery of NGLs with a proper control strategy and without composition analyzers. In particular, the effects of a combined cascade control with boilup approximation plus a pressure compensator control for the demethanizer section, on the desired NGL extraction product targets are investigated and compared under feed flowrate disturbances with conventional direct temperature controllers. Overall, it is shown that the cascade control plus pressure compensator provides the best performance and the lowest energy consumption