Gas Plant Research Articles

Design and Optimization of a Novel Hybrid Membrane-Electrochemical Hydrogen Pump Process for Recovering Helium from NRU off Gas.

Due to the low boiling point of helium, the nitrogen-rich off gas of the nitrogen rejection unit (NRU) in the liquefied natural gas (LNG) plant usually contains a small amount of CH4, approximately 1-4% He, and associated gases, such as H2. However, it is difficult to separate hydrogen and helium. Here, we propose two different integrated processes coupled with membrane separation, pressure swing adsorption (PSA), and the electrochemical hydrogen pump (EHP) based on different sequences of hydrogen gas removal. Both processes use membrane separation and PSA in order to recover and purify helium, and the EHP is used to remove hydrogen. The processes were strictly simulated using UniSim Design, and an economic assessment was conducted. The results of the economic assessment show that flowsheet #2 was more cost-effective due to the significant reduction in the capacity of the compressor and PSA because of the pre-removal of hydrogen. Additionally, using the response surface methodology (RSM), a Box-Behnken design experiment was conducted, and an accurate and reliable quadratic response surface regression model was fitted through variance analysis. The optimized operating parameters for the integrated process were determined as follows: the membrane area of M101 was 966.6 m2, the permeate pressure of M101 was 100 kPa, and the membrane area of M102 was 41.2 m2. The maximum recovery fraction was 90.66%, and the minimum cost of helium production was 2.21 $/kg. Thus, proposed flowsheet #2 has prospects and value for industrial application.

Some Perspectives for the Gasification Process in the Energy Transition World Scenario

Energy demand has increased over the years due to population growth, industrial, and socio-economic developments, cornerstones of human civilization. Additionally, climate change alarms are placing the energy transition in the top concerns of intergovernmental organizations. Therefore, there are several reasons for concern regarding the need for a new paradigm in the world energy scenario. This perspective article focuses on the contribution that the gasification process may have in the global energy transition scenario. The perspectives for a full world energy transition are that it cannot be accomplished without a transportation fuel transition and an industry transition. Biomass gasification is a sustainable process that allows the production of a large range of commodities such as electricity and heat, biofuels, and chemicals. Meanwhile, some challenges such as tar, impurities, and soot must be overcome or at least limited to an acceptable minimum to promote the economic viability of the gasification plants before they can effectively contribute to the world energy transition. In this regard, further research should be made focused on improving the syngas quality and the economic viability of a biomass gasification plant. This can be achieved by several means including new reactor designs, advanced gasification processes (e.g., plasma gasification and supercritical water gasification), and intensifying the gasification process.

Multi-Strategy Enhanced Dung Beetle Optimizer and Its Application in Three-Dimensional UAV Path Planning

Path planning is a challenging, computationally complex optimization task in high-dimensional scenarios. The metaheuristic algorithm provides an excellent solution to this problem. The dung beetle optimizer (DBO) is a recently developed metaheuristic algorithm inspired by the biological behavior of dung beetles. However, it still has the drawbacks of poor global search ability and being prone to falling into local optima. This paper presents a multi-strategy enhanced dung beetle optimizer (MDBO) for the three-dimensional path planning of an unmanned aerial vehicle (UAV). First, we used the Beta distribution to dynamically generate reflection solutions to explore more search space and allow particles to jump out of the local optima. Second, the Levy distribution was introduced to handle out-of-bounds particles. Third, two different cross operators were used to improve the updating stage of thief beetles. This strategy accelerates convergence and balances exploration and development capabilities. Furthermore, the MDBO was proven to be effective by comparing seven state-of-the-art algorithms on 12 benchmark functions, the Wilcoxon rank sum test, and the CEC 2021 test suite. In addition, the time complexity of the algorithm was also analyzed. Finally, the performance of the MDBO in path planning was verified in the three-dimensional path planning of UAVs in oil and gas plants. In the most challenging task scenario, the MDBO successfully searched for feasible paths with the mean and standard deviation of the objective function as low as 97.3 and 32.8, which were reduced by 39.7 and 14, respectively, compared to the original DBO. The results demonstrate that the proposed MDBO had improved optimization accuracy and stability and could better find a safe and optimal path in most scenarios than the other metaheuristics.

A New Pressure-Based Modeling Approach for Early Leak Detection in Gas Processing Plants Using Machine Learning

Natural gas is composed mostly of methane, the simplest hydrocarbon molecule, with only one carbon atom. But most gas at the wellhead contains other hydrocarbon molecules known as Natural Gas Liquids (NGL). Heavier gaseous hydrocarbons such as propane (C3H8), normal butane (n-C4H10), isobutane (i- C4H10) and pentanes, may also be processed in gas plants and exported as Liquified Natural Gas (LNG). During operational services in gas plant from inlet to outlet piping, gas leaks tend to occur undetected at some points in the facility. Apart from loss of gas resources, leaks and venting at natural gas processing plants release other pollutants besides methane (e.g., benzene, hexane, hydrogen sulfide) that can threaten air quality and public health. Hence, the need for early detection of gas leaks by using appropriate Machine Learning (ML) models. Insight from existing general flow equations was used to develop a new modelling approach for Machine Learning, in a test case: Gas Plant JK – 52. Input gas pressure data is calibrated and evaluated for consistency in real-time. The data is then corrected for lag-time and used to compute tolerance. Indicated time of alarm is checked against events such as residual gas, supply, pumping, etc. Where alarm is eventless, leak is suspected and eventually confirmed, suggesting that action should be taken to mitigate against the leakage. Following the input of a split training dataset, different types of regressions were used for the machine learning before automating the system for real-time evaluation and detection. Linear regression provided a 39% test accuracy, which was considered too low. This led to the use of random forest regression, which provided a 95% test accuracy and was considered excellent. It is hoped that with continuing data acquisition in gas plants employing this algorithm, further modelling will become more predictive as machine learns from experience.

Model analysis of carbon fiber reinforcement properties for reinforced concrete beams to resist blast loads

Safety is paramount in Oil & Gas plants, and continuous monitoring and improvements ensure that all measures are taken to protect them. The purpose of this paper is to examine how composite materials can be used to improve the structural reinforcement of concrete beams. Concrete structural beams have been improved in the past by using varying Fiber Reinforced Properties (FRP). It has been investigated how Carbon Fiber Reinforced Properties (CFRP) composites perform under blast loads and how they behave, respond, and perform as reinforcement for reinforced concrete beams. The response of RC beams to blasts was analyzed using a software modelling program called ANSYS that can mimic RC beam properties when reinforced with CFRP in concrete structures. The reason CFRP was chosen was because its properties showed great potential and it is well suited for testing and analysis. As well as absorbing a lot of energy, this material is strong, elastomeric, and alkali-resistant. A numerical analysis and model analysis have been performed with the help of the ANSYS software program. In the experimental results, CFRP was found to increase the flexural and shear strength of RC beams. The RC beams reinforced with CFRP has outperformed RC beam (control beam) in factors such as in Deformation, Equivalent Stress, and Shear Stress by a minimum percentage difference of 0.784% and maximum of 7.09% depending on the layers of CFRP and load applied on the beams in each factor.

Early startup methodologies in gas plant commissioning: An analysis of effective strategies and their outcomes

Gas plant commissioning is a critical phase in the lifecycle of gas facilities, where the transition from construction to operational readiness occurs. Early startup methodologies have gained prominence as strategic approaches to enhance the efficiency and effectiveness of the commissioning process. This review explores the various methodologies employed during the early startup phase, focusing on their strategies and outcomes. Effective early startup methodologies prioritize proactive planning and execution, aiming to mitigate risks associated with the commissioning of complex gas processing systems. Strategies such as phased commissioning, where systems are brought online in a sequential manner, allow for systematic testing and integration of subsystems. This approach enables teams to identify and resolve issues early, reducing the potential for costly delays during full operation. Moreover, incorporating thorough training programs for operational staff before full-scale commissioning is vital. Ensuring that personnel are familiar with the systems, safety protocols, and emergency procedures enhances operational readiness and contributes to a safer work environment. Additionally, leveraging digital tools and simulations during the commissioning phase can streamline processes and enhance collaboration among project stakeholders. Another critical aspect of early startup methodologies is the establishment of robust communication channels among all teams involved in the commissioning process. Effective communication fosters a collaborative environment, ensuring that challenges are promptly addressed and that all stakeholders are aligned on project goals and timelines. The outcomes of implementing early startup methodologies are significant. Facilities that adopt these strategies often experience reduced commissioning timeframes, minimized operational risks, and improved overall performance during initial operation phases. Furthermore, the successful execution of early startups contributes to enhanced reliability and safety standards in gas plant operations. In conclusion, early startup methodologies in gas plant commissioning are essential for achieving operational efficiency and safety. By focusing on strategic planning, personnel training, effective communication, and phased execution, gas facilities can optimize their commissioning processes and enhance long-term operational success.

Jetty Extension Study Due to Addition of Power Capacity on Thermal Water Dispersion at PLTGU Grati Pasuruan

PLTGU (Gas and Steam Power Plant) is a combination of gas power plant and steam power plant. PLTGU requires a cooling water systems during its operational life. The required amount of cooling water will increased along to increased power capacity of power plant. This project contains jetty extension study due to the addition of power capacity on thermal water dispersion at PLTGU Grati, Pasuruan. The results show using Delft3D hydrodynamic model, The alternative model showing quite good performance decreased temperature of cooling water on average 0.7975 oC at inlet canal. But it only need 2.5 oC increase of seawater temperature to decrease 0.33% efficiency, so it might change nothing to thermal power plant efficiency compared to the model result. Considering to the increasing risk of sedimentation rate, this alternative might be poor layout but further research needed. Still the best choice is on existing jetty design.

Life cycle assessment on improvement strategies of environmental sustainability for a liquified natural gas plant in Indonesia

Life cycle assessment on improvement strategies of environmental sustainability for a liquified natural gas plant in Indonesia

Revealing effective regional decarbonisation measures to limit global temperature increase in uncertain transition scenarios with machine learning techniques

Climate change mitigation scenarios generated by integrated assessment models have been extensively used to support climate change negotiations on the global stage. To date, most studies exploring ensembles of these scenarios focus on the global picture, with more limited attention to regional metrics. A systematic approach is still lacking to improve the understanding of regional heterogeneity, highlighting key regional decarbonisation measures and their relative importance for meeting global climate goals under deep uncertainty. This study proposes a novel approach to gaining robust insights into regional decarbonisation strategies using machine learning techniques based on the IPCC SR1.5 scenario database. Random forest analysis first reveals crucial metrics to limit global temperature increases. Logistic regression modelling and the patient rule induction method are then used to identify which of these metrics and their combinations are most influential in meeting climate goals below 2 °C or below 1.5 °C. Solar power and sectoral electrification across all regions have been found to be the most effective measures to limit temperature increases. To further limit increase below 1.5 °C and not only 2 °C, decommissioning of unabated gas plants should be prioritised along with energy efficiency improvements. Bioenergy and wind power show higher regional heterogeneity in limiting temperature increases, with lower influences than aforementioned measures, and are especially relevant in Latin America (bioenergy) and countries of the Organisation for Economic Co-operation and Development and the Former Soviet Union (bioenergy and wind). In the future, a larger scenario ensemble can be applied to reveal more robust and comprehensive insights.

Interplay between the Brassica napus phytoglobin (BnPgb1), folic acid, and antioxidant responses enhances plant tolerance to waterlogging

Oxygen deprivation by waterlogging reduces the productivity of several crop species, including the oil-producing crop Brassica napus L., which is highly sensitive to excess moisture. Among factors induced by oxygen deficiency are phytoglobins (Pgbs), heme-containing proteins known to ameliorate the response of plants to the stress. This study examined the early responses to waterlogging in B. napus plants over-expressing or down-regulating the class 1 (BnPgb1) and class 2 (BnPgb2) Pgbs. The depression of gas exchange parameters and plant biomass was exacerbated by the suppression of BnPgb1, while suppression of BnPgb2 did not evoke any changes. This suggests that natural occurring levels of BnPgb1 (but not BnPg2) are required for the response of the plants to waterlogging. Typical waterlogging symptoms, including the accumulation of reactive oxygen species (ROS) and the deterioration of the root apical meristem (RAM) were attenuated by over-expression of BnPgb1. These effects were associated with the activation of antioxidant system and the transcriptional induction of folic acid (FA). Pharmacological treatments revealed that high levels of FA were sufficient to revert the inhibitory effect of waterlogging, suggesting that the interplay between BnPgb1, antioxidant responses and FA might contribute to plant tolerance to waterlogging stress.

Financial Feasibility Assessment for PT ABC LNG Production Facility Expansion Plan

PT ABC is an LNG producer that has been producing a reliable LNG for its customers since 2013. Initial GSA with upstream parties is set to be expired in 2026, however, recent development indicates that upstream parties have secured PSC extension for another 20 years. Considering this development, PT ABC is considering an opportunity to secure a long-term gas supply that enables production capacity expansion which will create additional revenue and improve plant reliability. A technical feasibility study has been performed and 2 options with estimated construction time of 30 months are considered feasible. This final project aims to provide a financial feasibility assessment of the production capacity expansion project with capital budgeting and risk assessment technique. Capital budgeting analysis using base case scenario indicates that both options will provide additional value to DSLNG with an estimated NPV 52.1 MUSD, IRR 20.57%, DPBP 9.4 years, and PI 1.52 for Option 1, and estimated NPV 117.8 MUSD, IRR 28.77%, DPBP 8.62 years, and PI 2.68 for Option 2. However, this study also found that some variables are affecting the project financial feasibility significantly, such as LNG and feed gas price, plant availability, Capex realization and long-term debt interest rate. Monte Carlo simulation shows that the project has a probability of success (NPV > 0) of 54.24% for Option 1 and 65.80% for Option 2.

A simulation study on the condensation flow and thermal control characteristics of mixed refrigerant in a dimpled tube

In this study, the condensation flow and heat transfer characteristics of ethane/propane in the dimple tube have been numerically investigated, and the liquid droplet entrainment caused by the dimple structure was considered. The effects of operation and structure parameters on flow and heat transfer were analyzed, including different dimple depths, helical pitch, and dimple pitch. The results showed that the dimple tube had a higher heat transfer coefficient compared to the smooth tube, which increased with the increase in the mass flow rate and the decrease in the saturation temperature. The effect of dimple depth on heat transfer is greater than that of the dimple pitch or helical pitch, and the dimple depth will cause the local mass transfer rate to increase under high vapor quality. Meanwhile, the existing heat transfer correlations for smooth tubes were compared, and a condensation heat transfer correlation suitable for the mixed refrigerant in the dimple tubes was proposed with an average deviation of 6.3%.The conclusions obtained can provide support for the optimization design of liquefied natural gas plants.

Multi-Objective Based Generation Expansion Planning for Utility Power System of Tamil Nadu State Considering Recuperation of Older Power Plants

Generation Expansion Planning (GEP) is a problem that comprises multiple contradictory features while planning to construct new generating units. It must be solved by considering cost, reliability and environmental emission. Hence the mathematical representations have been developed to be accurate, and to improve the understanding of the multifaceted and contradictory aspects of the GEP problem. In this study, the Multi-Objective Comprehensive Learning Particle Swarm Optimization (MOCLPSO) algorithm is implemented for solving Multi-Objective GEP (MOGEP) problem. The objectives such as minimization of overall cost, decrement of the pollutant emission and enhancement of reliability have been considered by considering the constraints. The real-world MOGEP problem has been solved for seven-year (from the year 2020 to 2027) and fourteen-year (from the year 2020 to 2034) planning span for the utility power system of Tamil Nadu state, India. The problem is solved for four different cases with the consideration of retirement and recuperation of the older generating units. coal, gas, oil, nuclear, hydel, wind, Solar-photovoltaic (SPV) and biomass power plants were considered in this planning study. The results establish the competence of MOCLPSO to produce well-spread Pareto optimal non-dominated solutions of the MOGEP problem.

Gasification of MSW and Biomass using Aspen Plus®

Municipal Solid Waste (MSW) refers to solid waste generated by towns and cities from different types of household activities1. Over 2 billion tons of MSW are produced annually. Improper disposal can lead to adverse health outcomes through water, soil and air contamination. Hazardous waste or unsafe waste treatment such as open burning can directly harm waste workers or other people involved in waste burning and neighbouring communities. At the same time, in order to keep up with the need in development, the energy demand also increasing. Therefore, utilize MSW to produce energy is gaining more recognition from public interest. Gasification offers some advantages over traditional method of utilize MSW (incineration, compost). Gasification plants produce significantly lower quantities of air pollutants. The process reduces the environmental impact of waste disposal because it allows for the use of waste products as a feedstock. In this paper, Aspen Plus software was deployed to assess and predict the outcome of the gasification process of MSW. The model was calibrated and validated with various observed data. The condition of input MSW and biomass, as well as the gasification agent were considered. The results revealed that primary products of gasification process are similar to other previously conducted experiments.

Carbon-Energy Impact Analysis of Heavy Residue Gasification Plant Integration into Oil Refinery

A gasification plant may partially replace an industrial thermal plant and hydrogen production plant by polygenerating valuable products (hydrogen, power, steam) from low-value materials. Carbon energy analysis is one way of conceptually evaluating such processes. In this paper, the integration of a heavy residue (HR) gasification plant into a mid-size oil refinery (5 million t per year crude processing rate) is conceptually assessed via the comparison of electricity, natural gas and heavy residue consumption, and CO2 emissions. The main purpose of the integration is to reduce the consumption of natural gas currently used for hydrogen production at the expense of increased HR consumption and to achieve a reduction in CO2 emissions. Two case studies with different modes of operation were compared to base case showing that annual reduction of 2280 GWh in natural gas consumption with constant heat and hydrogen production is possible, accompanied with a slight increase in electricity purchase by 28 GWh per year. HR processing in the refinery increases by over 2800 GWh per year. The refinery’s CO2 emissions increase by more than 20% (up to 350 kt per year) as a result, while, after incorporating external emissions into the balance, a decrease of more than 460 kt CO2 per year can be achieved. This confirms that the integration of gasification plants within industrial enterprises and clusters has a positive environmental and energy impact and supports the idea of converting low-value material to more valuable products in polygeneration plants. The economics of HR gasifier integration in varying operations under real refinery conditions remain to be explored.

A Distributed IoT Air Quality Measurement System for High-Risk Workplace Safety Enhancement.

The safety of an operator working in a hazardous environment is a recurring topic in the technical literature of recent years, especially for high-risk environments such as oil and gas plants, refineries, gas depots, or chemical industries. One of the highest risk factors is constituted by the presence of gaseous substances such as toxic compounds such as carbon monoxide and nitric oxides, particulate matter or indoors, in closed spaces, low oxygen concentration atmospheres, and high concentrations of CO2 that can represent a risk for human health. In this context, there exist many monitoring systems for lots of specific applications where gas detection is required. In this paper, the authors present a distributed sensing system based on commercial sensors aimed at monitoring the presence of toxic compounds generated by a melting furnace with the aim of reliably detecting the insurgence of dangerous conditions for workers. The system is composed of two different sensor nodes and a gas analyzer, and it exploits commercial low-cost commercially available sensors.

Applied mineralogical investigation on coal gasification ash

This work aims to perform the applied mineralogical characterization of coal gasification ash (CGA) generated from a commercial fixed bed downdraft gasification plant in eastern India. Analytical and characterization techniques such as stereomicroscopy, optical microscopy, field emission scanning electron microscopy (FESEM) with energy dispersive spectroscopy (EDS) attached, X-ray fluorescence (XRF), energy dispersive x-ray spectrometer associated scanning electron microscope (SEM-EDX), X-ray diffraction (XRD), and thermogravimetric analysis (TGA) were applied for analyzing the coal and the corresponding gasification ash sample. These analytical techniques illustrate that SiO2 and Al2O3 are the major gasification ash phases, accounting for almost 85% of the entire CGA composition. The dominant mineral phases, such as quartz, mullite, and other aluminosilicates, provide an opportunity for utilization in construction and refractory material manufacturing. Moreover, confirming the presence of rare and valuable earth elements (RVEEs) in the CGA sample with SEM-EDX analysis unearths a new application window in meeting the requirement of the RVEEs starved nation like India, where very few commercial-scale coal gasification units are operational.

EPA proposal targets power plant CO2

A new US Environmental Protection Agency proposal aims to dramatically slash carbon emissions from coal-fired and natural gas–fired power plants. The regulations would require these plants to cut nearly all their carbon dioxide emissions by 2035. The proposal doesn’t specify how plants should meet the new regulations, but the agency says that it considered readily available emission control technologies when developing the new pollution standards. For example, companies can fit coal and gas plants with carbon capture and storage systems, which capture CO 2 before it leaves the smokestack. Older plants can be retired. According to a fact sheet provided by the EPA, the power sector is one of the country’s largest sources of greenhouse gases, responsible for 25% of emissions in 2021. By targeting this sector, the plan would prevent 617 million metric tons of CO 2 from entering the atmosphere by 2042, the EPA estimates. The proposal is

Costs of Gasification Technologies for Energy and Fuel Production: Overview, Analysis, and Numerical Estimation

During recent years, gasification technology has gained a high potential and attractiveness to convert biomass and other solid wastes into a valuable syngas for energy production or synthesis of new biofuels. The implementation of real gasification facilities implies a good insight of all expenses that are involved, namely investments required in equipment during the project and construction phases (capital expenditures, CapEx) and costs linked to the operation of the plant, or periodic maintenance interventions (operational expenditures, OpEx) or costs related to operations required for an efficient and sustainable performance of a gasification plant (e.g., feedstock pre-treatment and management of by-products). Knowledge of these economic parameters and their corresponding trends over time may help decision-makers to make adequate choices regarding the eligible technologies and to perform comparisons with other conventional scenarios. The present work aims to provide an overview on CapEx associated with gasification technologies devoted to convert biomass or solid waste sources, with a view of reducing the carbon footprint during energy generation or production of new energy carriers. In addition, an analysis of technology cost trends over time using regression methods is also presented, as well as an evaluation of specific capital investments according to the amount of output products generated for different gasification facilities. The novelty of this work is focused on an analysis of CapEx of existing gasification technologies to obtain distinct products (energy and fuels), and to determine mathematical correlations relating technology costs with time and product output. For these purposes, a survey of data and categorization of gasification plants based on the final products was made, and mathematical regression methods were used to obtain the correlations, with a statistical analysis (coefficient of determination) for validation. Specific investments on liquid biofuel production plants exhibited the highest decreasing trend over time, while electricity production became the least attractive solution. Linear correlations of specific investment versus time fitted better for electricity production plants (R2 = 0.67), while those relating the product output were better for liquid biofuel plants through exponential regressions (R2 = 0.65).

Dynamic Simulation of a Gas and Oil Separation Plant with Focus on the Water Output Quality

Gas and oil separation plants are the first main step in the production of hydrocarbon products. Depending on the properties of the recovered components from the well heads, and the physical properties in the underground rock reservoir, the plant design can vary in different ways. In mature oil and gas fields, secondary recovery methods are often used, which include the injection of large amounts of water into the underground reservoir, to induce the production flow of the wells. The handling of this water is of significant interest, in terms of production efficiency and pollution reduction, because the water comes into contact with the environment during and after recovery operations. In this work, a model of an exemplary gas and oil separation plant was created in Aspen HYSYS V10. A particular focus was placed on the modeling of oil residues in the water-bearing plant components. This model was then extended by the implementation of different process control schemes, to create a predictive model that could represent dynamic operating states in the plant components. Two different dynamic changes were then simulated using this model, to showcase the capabilities and capacities of the model.