Gas Enthalpy Research Articles

Analysis of the technical condition of gas turbine plants. Part 3. Application of machine learning methods

A method for estimating the technical condition coefficient of a gas turbine unit for transporting of natural gas using machine learning methods is considered. Archival gas-dynamic parameters recorded by the installation’s automatic control system are used as initial data. The initial data set was created by determining power from the change in enthalpy of natural gas before and after the blower. The software package is implemented in Python; the Scikit-learn library is used for machine learning models. The average absolute error in percentage is chosen as a criterion for the quality of the prediction. The prediction quality of machine learning models is assessed for different sets of parameters-features and sample sizes. Recommendations on the use of models are given. Keywords: gas turbine unit, technical condition, measuring system, data processing, machine learning. v.l.blinov@urfu.ru; deryabingleb.were@gmail.com

On the Development of Descriptor-Based Machine Learning Models for Thermodynamic Properties: Part 2—Applicability Domain and Outliers

This article investigates the applicability domain (AD) of machine learning (ML) models trained on high-dimensional data, for the prediction of the ideal gas enthalpy of formation and entropy of molecules via descriptors. The AD is crucial as it describes the space of chemical characteristics in which the model can make predictions with a given reliability. This work studies the AD definition of a ML model throughout its development procedure: during data preprocessing, model construction and model deployment. Three AD definition methods, commonly used for outlier detection in high-dimensional problems, are compared: isolation forest (iForest), random forest prediction confidence (RF confidence) and k-nearest neighbors in the 2D projection of descriptor space obtained via t-distributed stochastic neighbor embedding (tSNE2D/kNN). These methods compute an anomaly score that can be used instead of the distance metrics of classical low-dimension AD definition methods, the latter being generally unsuitable for high-dimensional problems. Typically, in low- (high-) dimensional problems, a molecule is considered to lie within the AD if its distance from the training domain (anomaly score) is below a given threshold. During data preprocessing, the three AD definition methods are used to identify outlier molecules and the effect of their removal is investigated. A more significant improvement of model performance is observed when outliers identified with RF confidence are removed (e.g., for a removal of 30% of outliers, the MAE (Mean Absolute Error) of the test dataset is divided by 2.5, 1.6 and 1.1 for RF confidence, iForest and tSNE2D/kNN, respectively). While these three methods identify X-outliers, the effect of other types of outliers, namely Model-outliers and y-outliers, is also investigated. In particular, the elimination of X-outliers followed by that of Model-outliers enables us to divide MAE and RMSE (Root Mean Square Error) by 2 and 3, respectively, while reducing overfitting. The elimination of y-outliers does not display a significant effect on the model performance. During model construction and deployment, the AD serves to verify the position of the test data and of different categories of molecules with respect to the training data and associate this position with their prediction accuracy. For the data that are found to be close to the training data, according to RF confidence, and display high prediction errors, tSNE 2D representations are deployed to identify the possible sources of these errors (e.g., representation of the chemical information in the training data).

General Equation to Estimate the Physicochemical Properties of Aliphatic Amines.

Changes in various physicochemical properties (P(n)) of aliphatic amines (including primary, secondary, and tertiary amines) can be roughly divided into nonlinear (P(n)) and linear (PLC(n)) changes. In our previous paper, nonlinear and linear change properties of noncyclic alkanes all were correlated with four parameters, n, SCNE, ΔAOEI, and ΔAIMPI, indicating number of carbon atoms, sum of carbon number effects, average odd-even index difference, and average inner molecular polarizability index difference, respectively. To date, there has been no general equation to express changes in the properties of substituted alkanes. This work, based on the molecular structure characteristics of aliphatic amine molecules, proposes a general equation to express nonlinear changes in their physicochemical properties, named as the "NPAA equation" (eq 12), ln(P(n)) = a + b(n) + c(SCNE) + d(ΔAOEI) + e(PEI) + f(APEI) + g(GN), and proposes a general equation to express linear changes in the physicochemical properties of them, named as the "LPAA equation" (eq 13), PLC(n) = a + b(n) + c(SCNE) + d(ΔAOEI) + e(PEI) + f(APEI) + g(GN). In NPAA and LPAA equations, a, b, c, d, e, f, and g are coefficients, and PEI, APEI, and GN represent the polarizability effect index, average polarizability effect index, and N atomic influence factor, respectively. The results show that nonlinear and linear change properties of aliphatic amines all can be correlated with six parameters, n, SCNE, ΔAOEI, PEI, APEI, and GN. NPAA and LPAA equations have the advantages of uniform expression, high estimation accuracy, and usage of fewer parameters. Further, by employing the above six parameters, a quantitative correlation equation can be established between any two properties of aliphatic amines. Using the obtained equations as model equations, the property data of aliphatic amines were predicted, involving 107 normal boiling points, 10 refractive indexes, 11 liquid densities, 54 critical temperatures, 54 critical pressures, 62 liquid thermal conductivities, 59 surface tensions, 56 heat capacities, 55 critical volumes, 54 gas enthalpies of formation, and 57 gas Gibbs energies of formation, a total of 579 values, which have not been experimentally determined yet. This work not only provides a simple and convenient method for estimating or predicting the properties of aliphatic amines but can also provide new perspectives for quantitative structure-property relationships of substituted alkanes.

Turbocompound energy recovery option on a turbocharged diesel engine

The transportation sector is living a transition era in which hybrid and electrified vehicles are replacing conventional vehicles, based on internal combustion engines. This is pushed by the recognized need for reducing fuel consumption and tailpipe emissions, considering primary pollutants and carbon dioxide as a greenhouse gas. In the transition path, hybridization and partial electrification of the powertrain play a crucial role. In this regard, the need for on-board electrical energy storage and utilization is increasing significantly and the possibility to recover wasted energy and convert it into electrical form is mandatory. This is especially true for commercial and heavy-duty vehicles, where full electrification is more difficult to be implemented. Waste Heat Recovery (WHR) has therefore become so important for vehicles, not only to directly reduce fuel consumption and related emissions but also to improve the feasibility of a generation of vehicles with a higher degree of hybridization that considers, for example, the electrification of auxiliaries following the so-called auxiliaries-on-demand management. Wasted heat refers mainly to exhaust heat from gases, where about one third of the fuel energy is disposed of. Among the various systems for WHR, engine turbo-compounding is approaching a mature technology. This technological option makes use of an additional turbine on the exhaust line of the engine, downstream of the turbocharging one, which converts the residual gas enthalpy into mechanical form. In this paper, the F1C Iveco 3.0 L turbocharged diesel engine is considered for verifying the performances of a turbo-compounding system. The engine was mounted on a dynamic engine test bench. In particular, the interactions with the original engine produced on the exhaust line were studied. Backpressure effects on the engine introduced by turbo-compounding were evaluated reversed in terms of extra fuel consumption. Moreover, the new equilibrium of the turbocharger was assessed and the related modifications to the engine were measured considering that the turbocharger has a control strategy based on the so-called Variable Geometry Turbine (VGT), via the modification of the Inlet Guide Vanes (IGV). The presence of a secondary turbine for WHR opens to a wider possibility of actuating the IGV and, so, the possibility to optimize the recovery considering the integrated system and all its degrees of freedom.

Uncertainty in Modeling Ablation Heat Transfer in Rocket Nozzles

The ablation of carbon cloth phenolic insulators used in solid rocket motor (SRM) nozzles involves highly complex phenomena that are difficult to accurately predict. Historical and even more modern ablation predictions rely heavily on anchoring to SRM test data to improve predictability and SRM reliability. Accelerated schedules, reductions in static SRM testing prior to flight, and a highly competitive global market are placing a substantial onus on computational capability and predictive uncertainty. Quantifying uncertainty in SRM nozzle ablation predictions is essential for motor reliability. This paper provides the details of a modern uncertainty quantification methodology applied to ablation predictions in carbon cloth phenolic insulators exposed to SRM nozzle environments. A particular historical test motor is used as a demonstration case. The system response quantities of interest are the erosion depth and char depth. A representative model and input uncertainty are provided, and a sensitivity analysis is performed to identify influential parameters. Uncertainties in the numerical models and inputs are propagated through a two-dimensional uncertainty quantification methodology using a Latin Hypercube Sampling approach. The results show that the primary sources of uncertainty in SRM nozzle thermal modeling are the heat transfer coefficient, incident radiation heat flux, char material thermal conductivity, virgin material density, char material density, char material specific heat, and pyrolysis gas enthalpy. Uncertainties in the predictions of nozzle insulation erosion and char for the test case are provided relative to the nozzle location at the 99 th percentile and 95th confidence interval. Uncertainty in the char depth is roughly ±28% along the entire axial length of the nozzle. Uncertainty in the erosion depth ranges from about ±45% for the entrance and throat regions to ±80% at the nozzle exit.

Analysis of CO2 capture process from flue-gases in combined cycle gas turbine power plant using post-combustion capture technology

Combined cycle gas turbine power plants (CCGTs) are a combined cycle consisting of a gas turbine and a steam turbine to generate electricity. Sometimes CCGTs incorporate cogeneration to produce both heat and power. After the gas fuel combustion in the gas turbine combustion chamber, the flue gases are passed through the Heat Recovery Steam Generator (HRSG) to extract heat and generate additional electrical power using the steam turbine. The CCGT technology is recognized for its highest efficiency of 58% in electricity production among the power production technology. A highly efficient CCGT power plant with 60% efficiency produces even 2.5 times less CO2 than a modern coal power plant with an efficiency of 45% because of the use of gas fuel and electrical efficiency. The CO2 emission can be reduced further when the post-combustion CO2 capture methods are applied. To perform CO2 capture and to reduce the CO2 emission from power plants, the CO2 mass fraction in flue gas is the crucial parameter for the operation of the Post-combustion Carbon Capture and Storage (PCCS) technology. The PCCS technology uses solvents, which is the aqueous solution of amine that reacts with flue gas and absorbs the CO2, which is treated and separated later. The paper presents the results of the energy analysis of a post-combustion carbon capture process when integrated with a combined cycle gas power plant. The study considered two different gas fuels such as methane and syngas. Syngas composition was determined from the sewage sludge gasification process and can be treated as zero-emission CO2 gas fuel. When the flue gas produced from the syngas is used in PCCS at more than 50% of load conditions, the negative CO2 emission level in large-scale CCGT power plants can be reached. The paper presents the results of mass and energy balance analysis of HRSG of a CCGT integrated with PCCS to perform CO2 capture. The analysis of HRSG using flue gases from methane and syngas is performed by calculating the enthalpy of flue gas, rate of heat exchange, and temperature distribution of each component of HRSG. The comparison of the result shows slight differences due to the different composition and flow rates of flue gases. The flue gas at the outlet of two HRSG is used in the PCCS at different load conditions. An aqueous solution of 30 wt% Monoethanolamine (MEA) with rich loading of 0.5 mol-CO2/mol-MEA and a mixture of 16 wt% 2-amino-2-methyl-1-propanol (AMP) – 14 wt% Piperazine (PZ) with rich loading of 0.62 and 0.86 mol-CO2/mol-amine respectively are used in the PCCS. Due to the reboiler duty of amines, the steam consumed by the PCCS for AMP-PZ regeneration is less compared to MEA. Depending upon the flue gas and solvent used in the PCCS, the power consumed by the PCCS from CCGT power plant is measured from 9.6% to 11.6%. For the given operating condition of the CCGT and PCCS at more than 50% load condition, the negative CO2 emission level can be achieved.

Artificial neural network models for heat transfer in the freeboard of a bubbling fluidised bed combustion system

The heat transfer in the combustion chamber (freeboard) of a fluidised bed combustor is of critical importance, due to the need for thermal efficiency, the possible environmental impacts, and the increasing costs of fuel and electricity. In this study, an artificial neural network model was used to analyse the heat transfer in a bubbling fluidised bed combustion system. The heat transfer of the system was investigated using temperature measurements of the combustion chamber and side wall of a 0.3 MWe fluidised bed combustor, the fly ash contents (%) of 40 types of coal, the gas absorption coefficients (CO2 and H2O vapour), and the enthalpy of the flue gas. The whole system was analysed using a backpropagation algorithm (10 hidden layers with 10 neurons). It was found that the most important factor when calculating the heat transfer in the combustion chamber was the height of the chamber, and the influence of the flue gas enthalpy and the ash particle content on the heat transfer accounted for 4% and 8.6%, respectively. The accuracy of the results from the proposed multilayer perceptron algorithm is high, and analyses could be performed for different values of the input data.

Towards the Development of Miniature Scale Liquid Fuel Combustors for Power Generation Application—A Review

As the demand for powerful, light energy sources continues to grow, traditional electrochemical batteries are no longer sufficient and combustion-based power generation devices have become an attractive alternative due to their high energy density, compact size, fast recharging time and long service life. While most research on miniature-scale combustors has focused on gaseous fuels, the use of commonly available liquid fuels has the potential to be highly portable and economical. However, the complexity of droplet atomization, evaporation, mixing and burning in a limited volume and short residence time has presented significant challenges for researchers. This review focuses on various methodologies proposed by researchers (like flow burring injector, fuel film injection, injecting into porous media, electrospray and some self-aspirating designs) to overcome these challenges, the combustion behaviour and different instabilities associated with liquid fuels at small scales. The current review intends to present a clear direction to channel the efforts made by researchers to overcome the difficulties associated with liquid fuel combustion at small scales for power generation applications. Additionally, this review aims to give an overview of power systems at the micro and meso scales that operate using liquid fuels. The methodologies introduced like electrospray requires external power, which again makes the system complex. Towards the development of standalone type power generators, the self-aspirating design which makes use of hydrostatic pressure, fuel film injection or taking advantage of exhaust gas enthalpy to preheat and evaporate the liquid fuel are the promising methodologies.

Burner-heated dehydrogenation of a liquid organic hydrogen carrier (LOHC) system

For a hydrogen-based economy, safe and efficient hydrogen storage is essential. Compared to other chemical hydrogen storage technologies, such as ammonia or methanol, liquid organic hydrogen carrier (LOHC) systems allow for a reversible storage of hydrogen while being easy to handle in a diesel-like manner. In our contribution, we describe for the first time the successful utilization of the exhaust gas enthalpy of a porous media burner to directly supply the dehydrogenation heat for a kW-scale dehydrogenation of the hydrogen-rich LOHC compound perhydro dibenzyltoluene (H18-DBT). Our setup demonstrates the dynamics of the dehydrogenation unit at a realized maximum hydrogen power of 3.9 kWth, based on the lower heating value of the released hydrogen. For the intended applications with fluctuating hydrogen demand, e.g. a hydrogen refueling station (HRS) or stationary heating in buildings, a dynamic hydrogen supply from LOHC is important. Methane, e.g. from a biogas plant, is utilized in our scenario as a fuel source for the burner. Hydrogen is released within 30 min after cold start of the system. The dehydrogenation unit exhibits a power density relative to the reactor volume of about 0.5 kWtherm l−1 based on the lower heating value of the hydrogen and a catalyst productivity of up to 0.65 gH2 gPt−1 min−1 for hydrogen release from H18-DBT. An analysis of the by-products and reaction intermediates shows low by-product formation (e.g. maximum 0.6 wt.-% for high boilers and 0.9 wt.- % for low boilers) and uniform distribution of intermediates after the reaction. Thus, a relatively homogeneous temperature distribution and a uniform LOHC flow in the reaction zone can be assumed. Our findings illustrate the dynamics (heating rates of about 10 K min−1) and performance of direct heating of a release unit with a burner and represent a significant step towards LOHC-based hydrogen provisioning systems at technically relevant scales.

Thermodynamic performance comparison of SOFC-MGT-CCHP systems coupled with two different solar methane steam reforming processes

Multi-energy complementary distributed energy system integrated with renewable energy is at the forefront of energy sustainable development and is an important way to achieve energy conservation and emission reduction. A comparative analysis of solid oxide fuel cell (SOFC)-micro gas turbine (MGT)-combined cooling, heating and power (CCHP) systems coupled with two solar methane steam reforming processes is presented in terms of energy, exergy, environmental and economic performances in this paper. The first is to couple with the traditional solar methane steam reforming process. Then the produced hydrogen-rich syngas is directly sent into the SOFC anode to produce electricity. The second is to couple with the medium-temperature solar methane membrane separation and reforming process. The produced pure hydrogen enters the SOFC anode to generate electricity, and the remaining small amount of fuel gas enters the afterburner to increase the exhaust gas enthalpy. Both systems transfer the low-grade solar energy to high-grade hydrogen, and then orderly release energy in the systems. The research results show that the solar thermochemical efficiency, energy efficiency and exergy efficiency of the second system reach 52.20%, 77.97% and 57.29%, respectively, 19.05%, 7.51% and 3.63% higher than those of the first system, respectively. Exergy analysis results indicate that both the solar heat collection process and the SOFC electrochemical process have larger exergy destruction. The levelized cost of products of the first system is about 0.0735$/h that is lower than that of the second system. And these two new systems have less environmental impact, with specific CO2 emissions of 236.98 g/kWh and 249.89 g/kWh, respectively.

Liquid Organic Hydrogen Carriers Applied on Methane–Hydrogen-Fueled Internal Combustion Engines: A Preliminary Analysis of Process Heat Balance

One of the main issues that has limited the use of hydrogen as an energy vector for a long time is its low energy density per unit of volume. Alternative chemical storage methods have been developed in recent years to overcome the limitations associated with compressed or liquified hydrogen storage. One of these is the Liquid Organic Hydrogen Carrier (LOHC), which utilizes organic hydrocarbons that can capture hydrogen (through an exothermic hydrogenation reaction) and release hydrogen (through an endothermic dehydrogenation reaction). In this paper, a 0D model of an internal combustion engine fueled with a mixture of hydrogen and methane was used to investigate whether the enthalpy of the exhaust gases can balance the heat rate required to self-sustain the dehydrogenation stage. Two LOHC+ compounds were considered, namely, Perhydro-dibenzyltoluene and Perhydro-N-Ethylcarbazole. Four different hydrogen-to-methane ratios were considered, assuming an engine maximum brake power ranging from 500 to 6000 RPM. An energy balance was performed, balancing the dehydrogenation heat rate and the exhaust gas cooling heat rate, in order to establish the minimum temperatures of the exhaust gases required to self-sustain the LOHC+ dehydrogenation. We demonstrated that the minimum exhaust temperatures required to self-sustain the process in different running regimes and at different hydrogen-to-methane ratios are lower than literature and experimental exhaust temperatures.

New Method of NPOH Equation-Based to Estimate the Physicochemical Properties of Noncyclic Alkanes.

Changes in various physicochemical properties (P (n)) of noncyclic alkanes can be roughly classified as linear and nonlinear changes. In our previous study, the NPOH equation was proposed to express nonlinear changes in the properties of organic homologues. Until now, there has been no general equation to express nonlinear changes in the properties of noncyclic alkanes involving linear and branched alkane isomers. This work, on the basis of NPOH equation, proposes a general equation to express nonlinear changes in the physicochemical properties of noncyclic alkanes, including a total of 12 properties, boiling point, critical temperature, critical pressure, acentric factor, heat capacity, liquid viscosity, and flash point, named as the "NPNA equation", as follows: ln(P (n)) = a + b(n - 1) + c(S CNE) + d (ΔAOEI) + f(ΔAIMPI), where a, b, c, and f are coefficients, and P (n) represents the property of the alkane with n carbon atom number. n, S CNE, ΔAOEI, and ΔAIMPI are number of carbon atoms, sum of carbon number effects, average odd-even index difference, and average inner molecular polarizability index difference, respectively. The obtained results show that various nonlinear changes in the properties of noncyclic alkanes can be expressed by the NPNA equation. Nonlinear and linear change properties of noncyclic alkanes can be correlated with four parameters, n, S CNE, ΔAOEI, and ΔAIMPI. The NPNA equation has the advantages of uniform expression, usage of fewer parameters, and high estimation accuracy. Furthermore, using the above four parameters, a quantitative correlation equation can be established between any two properties of noncyclic alkanes. Employing the obtained equations as model equations, the property data of noncyclic alkanes, involving 142 critical temperatures, 142 critical pressures, 115 acentric factors, 116 flash points, 174 heat capacities, 142 critical volumes, and 155 gas enthalpies of formation, a total of 986 values, were predicted, which have not be experimentally measured. NPNA equation not only can provide a simple and convenient estimation or prediction method for the properties of noncyclic alkanes but also can provide new perspectives for studying quantitative structure-property relationships of branched organic compounds.

A preliminary computational analysis towards the use of Electrically Heated Mixing Catalyst for innovative SCR after-treatment systems

This work aims at investigating the possible advantages of substituting the mixer in traditional SCR systems with an Electrically Heated Catalytic structure (EHC). First of all, EHC technology is being widely investigated in literature because it offers a concrete solution for catalyst thermal activation and film formation reduction in engine cold start conditions; however, its adoption as a mixer can also guarantee other important improvements in reducing pollutant emissions. In this work, a low-pressure injection of Ad-Blue impacts an electrically heated structure and a complete analysis of liquid droplets and film evaporation is carried out. A hybrid Eulerian-Lagrangian model has been adopted on a multi-region configuration, accounting for fluid-solid conjugate heat transfer (CHT), which plays a key role in the conversion strategy: it has been demonstrated that the heated structure can be exploited to significantly increase the exhaust gas enthalpy in the cold start, which represents an important improvement for pollutant conversion. Different heating strategies are analysed, with the objective of maximising the spray evaporation and the uniformity of the ammonia distribution downstream of the mixer. The objectives of this work are emphasizing the improvements that an electrically heated mixing catalyst can bring to the traditional SCR configuration, laying the foundation for other following studies.

A path to a dynamic hydrogen storage system using a liquid organic hydrogen carrier (LOHC): Burner-based direct heating of the dehydrogenation unit

Liquid organic hydrogen carrier (LOHC) systems offer a promising way to store and transport hydrogen in a safe and dense form at ambient conditions. Compared to other chemical hydrogen storage systems such as ammonia, formic acid, and methanol, LOHC systems enable reversible storage of hydrogen. For the endothermic hydrogen release reaction, heat and a suitable catalyst are needed. In this work for the first time, the exhaust gas enthalpy of a porous media burner is utilized to supply the heat for dehydrogenation of the LOHC compound perhydro dibenzyltoluene (H18-DBT). This approach significantly increases the system dynamics and the power density of the dehydrogenation unit compared to conventional heating via a heat transfer fluid. A dynamic hydrogen release is important to satisfy the fluctuating hydrogen demand, especially in hydrogen refuelling stations (HRS) or in stationary heating applications, e.g., for buildings. Our setup utilized methane as fuel source for the burner. The key challenge is the precise temperature control of the direct heating of the LOHC in the dehydrogenation unit. This is required as hot spots may result in thermal decomposition of the LOHC compounds, while cold spots limit the rate of the dehydrogenation reaction. With our laboratory-scale setup, we demonstrate a hydrogen release from H18-DBT of up to 450 mlN min−1 with 8.2 g of Pt on alumina catalyst (0.3 mass % Pt loading, i.e., 25 mg of Pt). Hydrogen is released within 10 min after a cold start of the entire system. The system exhibits a catalyst productivity of up to 1.5 gH2 gPt−1 min−1 and a power density relative to the reactor volume of about 3.6 kWtherm L−1 based on the lower heating value of hydrogen. Our findings show the strong potential of the direct heating concept in hydrogen release from LOHC systems and represent a significant step towards LOHC-based technical hydrogen provision systems.

Efficient separation of methane, ethane and propane on mesoporous metal-organic frameworks

Adsorption and separation of light saturated hydrocarbons (methane, ethane and propane) as main components of natural gas on a series of isoreticular mesoporous metal–organic frameworks NIIC-20-G (G = ethyleneglycol, 1,2-propyleneglycol, 1,2-butyleneglycol, 1,2-pentylenglycol, glycerol) has been thoroughly investigated. An impact of the size and nature of the glycol moiety on fundamental parameters of the adsorption (gas uptakes, adsorption constants, enthalpy and entropy, adsorption selectivity factors) was revealed and rationalized. The highest gas uptakes at 1 bar and 298 K are 13.2 ml(STP)·g−1 for CH4, 55.0 ml(STP)·g−1 for C2H6 and 125.4 ml(STP)·g−1 for C3H8. The IAST adsorption selectivities at ambient conditions (298 K, 1:1 gas mixture) reach 24.2 for C2H6/CH4, 29.0 for C3H8/C2H6 and as high as 1110 for C3H8/CH4. A rare combination of high adsorption uptakes and superb adsorption selectivity values achieved for NIIC-20-G put those MOFs ahead of the most other materials for light saturated hydrocarbon adsorption and separation. Dynamic breakthrough gas separation experiments on NIIC-20-Pr fully confirm effective separation of lighter alkanes from the corresponding binary or ternary mixtures. The obtained methane productivities are 1182 ml(STP)·g−1 (52.8 mol·kg−1) for C2H6/CH4, and 4193 ml(STP)·g−1 (187.3 mol·kg−1) for C3H8/CH4 equimolar gas mixtures, which greatly surpass earlier published data. The ethane productivity for an equimolar C3H8/C2H6 gas mixture is 3009 ml(STP)·g−1 (134.4 mol·kg−1). The breakthrough separation experiments validate a remarkable performance of the studied MOFs in the practical separation of natural gas or other relevant mixtures of light alkanes to valuable individual components.

Deep learning optimization of a combined CCHP and greenhouse for CO2 capturing; case study of Tehran

Although renewable-based energy systems have been increasing and attractive worldwide, the intermittent nature of renewable energy sources cannot cause fossil fuels to be completely removed from the energy systems. So, an attractive approach is to capture produced CO2 from existing power plants for several purposes for the status quo. One of these purposes is to use CO2 in greenhouses in the propinquity of power plants. In this context, a greenhouse combined with an absorption chiller and Organic Rankin Cycle was designed and analyzed in order to exert the high temperature exhausted gases enthalpy from the micro power plants. The produced CO2 is also separated using a catalytic converter and is employed for the greenhouse products to compensate for the lack of CO2 for the required standard for plant growth. Compared to similar systems, the present analysis is comprehensive enough in all energy, exergy, economic, and environmental perspectives. Besides, all functions are calculated using a deep artificial neural network algorithm, and two scenarios were considered for both summer and winter. Weather conditions data were collected during a 10-year interval to predict the system's behavior. After optimization, the CO2 production rate was declined by 56% and the maximum energy and exergy efficiencies were achieved by 47.3% and 36.6%, respectively. Also, a net annual interest of 23.4 M$ was achieved due to an increase in greenhouse harvest.

Estimation of gas turbine technical condition using machine learning methods

This paper considers the method to estimate the technical condition of gas turbine power for natural gas transportation, using machine learning methods. Source data was used to archive gas-dynamic parameters from the automatic control system of the gas turbine. The method is based on changing the enthalpy of the natural gas before and after the centrifugal gas compressor is used for creating a dataset with measured parameters and power from the gas turbine. The actual power is determined from the line of modes for a certain period. The software is implemented using Python and the Scikit-learn library is used to create machine learning models. A mean average percentile error is chosen as the model quality criterion. In this paper, different sets of feature parameters and sample sizes are researched by the quality of the prediction machine learning models. Recommendations on the use of models are given. It has been established that the approach is not applicable for predicting future technical condition without the presence of data on a similar technical condition in the training sample. It is recommended to use the described approach to determine the technical condition in a period of operation in the past.

Study on the solid particle erosion characteristic of top gas pressure recovery turbine under variable working condition

Aiming at the problem of particle erosion in top gas pressure recovery turbines, the physical and chemical properties of the sampled gas dust particles are tested in this paper, and particle motion behavior and erosion characteristics in the turbine cascade are investigated by employing a validated numerical method. On this basis, the influence mechanism of adjustable stator angle and rotate speed on the erosion characteristics of blades under variable working conditions is quantitatively explored. Results show that the erosion damage of the first-stage stator S1 and rotor R1 are mainly initiated by particles with different size. The change of adjustable stator angle will change the load and gas enthalpy drop distribution of the cascade, which then affects the impingement velocity and position of the particles. When adjustable stator angle reduces from 44.2 to 25% opening, erosion of S1 trailing edge grows about 20%, while the erosion of R1 trailing edge reduces by 50%. The change of rotating speed will change the relative magnitude of the particle centrifugal force and airflow drag force, which in turn affects the particle’s trajectory and impingement velocity. When rotate speed increases from 3,000 to 4,500 r/min, both the particle impingement velocity and escape rate increase, resulting in doubling of the erosion on blade leading edge and halving of the erosion on blade trailing edge. Finally, for the high rotation speed TRT operating under variable working conditions, some insightful suggestions are given to minimize the erosion damage of the turbine blades.

Quantitative determination of impact release energy for TiZrHfX0.3 multicomponent materials in vacuum environment

Multicomponent materials have been widely used in the field of national defense due to their excellent mechanical properties and impact energy release characteristics. In order to obtain the effect of trace element and impact velocity on energy release, TiZrHfCu0.3, TiZrHfAl0.3 and TiZrHfNi0.3 multicomponent materials are used as the research object. The response parameters such as perforation diameter and penetration depth, flash radiant temperature, gas overpressure, flame propagation velocity and vessel wall temperature rise induced by multicomponent materials projectile impacting on the target in vacuum environment are measured in a quasi-closed vessel. The deformation energy of target, mixed gas enthalpy in the vessel, flash radiant energy, absorbed energy of quasi-closed vessel wall and injected gas enthalpy are calculated theoretically, respectively. Then the impact energy release is determined quantitatively and compared with the calculating results of the Ames impact energy release model. The results show that the impact release energy of per unit mass projectile increases with the increasing of impact velocity, but the increase amplitude is small. However, the impact energy release capacity from the strongest to the weakest is TiZrHfCu0.3, TiZrHfAl0.3 and TiZrHfNi0.3, respectively. Because the energy loss is not considered in the Ames model, the calculating result of the Ames model is lower than that of this method.

Difluoromethane (HFC-32) and Pentafluoroethane (HFC-125) Sorption on Linde Type A (LTA) Zeolites for the Separation of Azeotropic Hydrofluorocarbon Refrigerant Mixtures.

Worldwide use of hydrofluorocarbons (HFCs) is currently being regulated and phased out because of high global warming potentials (GWPs). Separation techniques for recycling refrigerants are needed so that HFCs can be dealt with responsibly. Many HFCs currently in use are azeotropic or near-azeotropic refrigerant blends and must be separated so that the components can be recycled and repurposed effectively. One such refrigerant is R-410A, which is a near-azeotropic 50/50 wt % mixture of pentafluoroethane (HFC-125) and difluoromethane (HFC-32). This study examined the use of the LTA zeolites for separating HFC-32 from HFC-125. Pure gas isotherms were measured using a XEMIS gravimetric microbalance with zeolites 3A, 4A, and 5A. Reversible sorption was observed for HFC-32 with zeolites 4A and 5A, whereas irreversible sorption was observed for HFC-125 with zeolite 5A. Negligible sorption was observed for HFC-125 with zeolites 3A and 4A, and although sorption of HFC-32 with zeolite 3A was observed, the process was slow, making the sorbent not commercially viable. The enthalpy of adsorption was predicted using the vapor adsorption equilibrium (VAE) analogue of the Clausius-Clapeyron equation and measured using a calorimeter for HFC-125 and HFC-32 with zeolite 5A and for HFC-32 with zeolite 4A. Molecular-level interactions between the LTA zeolites and HFCs were discussed and used to interpret pure gas isotherms and enthalpy of adsorption results. Overall, zeolites 4A and 5A were found to be good candidates for kinetically and thermodynamically separating R-410A, respectively.