Exhaust Gas Temperature Values Research Articles

아두이노를 이용한 마이크로 가스터빈 엔진 시동 구간의 제어 로직 구현 연구

In this study, the starting sequence of the commercial micro gas turbine engine was analyzed and the control logic was developed and implemented using Arduino. The starting sequence the engine is composed of five steps: engine cranking, fuel supply, ignition, acceleration, and stabilization. The control logic of the starting sequence is to get the engine to reach a target(Idle) RPM by controlling the sub-components of the engine such as the start motor, fuel pump, glow plug, start valve, and the main valve. And these sub-components are controlled based on measured values of RPM and EGT(Exhaust Gas Temperature) at the start region. The start sequence of the micro turbine engine was analyzed by conducting a set of experiments using a commercial engine, Jetcat P300-RX and its ECU (Jettronics V10). Then the control logic was developed using Arduino and Arduino MEGA-based ECU was implemented onto the micro turbine engine system. MOSFET modules and motor drivers were used to control the sub-components and Hall sensor and K-type thermocouple were applied to RPM and EGT measurements, respectively. Effects of three parameters, fuel flow rate at ignition step, start motor acceleration rate, and fuel flow rate at acceleration step on engine behavior was investigated and optimal values for those parameters were obtained and applied to the control logic. The results showed that a successful starting sequence is achieved with a maximum EGT of less than 700℃ and a start time within 60 seconds using the developed control logic, which is quite similar to the commercial micro turbine behavior.

Performance Assessment of a Single-Cylinder Diesel Engine with Diesel and Waste Cooking Oil Blends Preheated by Engine Exhaust Gas

The present work explains the engine performance using waste cooking oil (WCO) blended with diesel. The viscosity of WCO can be reduced to be comparable with diesel by preheating at 90°C and thus improves the engine combustion efficiency. For the purpose of preheating, a shell and tube heat exchanger were designed. The optimized tube and shell diameters of the heat exchanger were estimated as 17 and 460 mm, respectively. Pressure drops in tube and shell sides changed from 28.40 - 27.40 Pa and 0.025 - 0.1 Pa respectively. Flue gas energy contents varied from 4000 W to 16000 W at the mass flow rate of 0.03-0.08 kg/s. Useful energy gained by the heat exchanger for waste cooking oil preheating varied from 1200-5500 W at 1200-3000 rpm. Kinematic viscosity for WCO was 51.85 cSt and it changed to 7.55 - 17.5 cSt for blends of 10 - 30% with diesel at 40°C. As the blending % of WCO with diesel increased, there was a minor drop in brake power because of lower calorific value of the mixture. There was a minor increment of torque because of preheating. Preheated fuel blends injection into the cylinder facilitated better fuel efficiency. Minimum and maximum values of exhaust gas temperature were 345°C and 385°C at 1200 and 3000 rpm of engine speeds respectively.

Comparative analysis of engine running performance with and without thermostat

The rate of removal of internal combustion (IC) engine thermostat when engines are imported to Ghana and other part of African continent is alarming. Such phenomenon calls for an experiment to compare the performance of IC engines imported here in Ghana running with and without engine thermostat. The analysis was done by determine engine performance characteristic such as engine torque, indicated power (Ip), brake power (bp), frictional power (fp), fuel consumption, exhaust gas temperature (EGT) as well as exhaust emission at engine speed of 1500 rpm for engine running with thermostat (WT) and without thermostat (WOT). Descriptive statistics and analysis of variance (ANOVA) were done using GenStat software (VSN International, 2021). Statistical significance was carried out at p≤0.05. The best fuel mean value of 103 ml was recorded for engine condition WT at EGT of 283.2 °C while fuel consumed for engine condition WOT was 170 ml at EGT of 155.4 °C. The recorded mean exhaust emission gases for Ex, O2, CO, H2S were 13.2%, 16.2%, 1000 ppm and 35.2 ppm and 0%, 18.38%, 393.2 ppm and 0.4 ppm for engine condition WOT and WT respectively. There was significant difference (p≤0.05) in mean values of EGT, Fuel consumption and exhaust emissions for engine condition WOT with the exception of O2. The removal of engine thermostat affect engine working temperature which result in incomplete combustion, high fuel consumption and high exhaust emissions.

Effect of H2 addition to methanol-gasoline blend on an SI engine at various lambda values and engine loads: A case of performance, combustion, and emission characteristics

The main purpose of this study was to investigate the effects of hydrogen and methanol addition on the engine performance, combustion, and emission characteristics of a spark-ignition engine. The experiments were performed with pure gasoline, methanol-gasoline, and hydrogen-methanol-gasoline fuel blend under various engine loads (25Nm, 50Nm, 75Nm, and 100Nm, 100%), different lambda values (0.8, 1, and 1.2), and a constant engine speed (2000 rpm). The hydrogen energy substitute rate method was applied for hydrogen enrichment that was constituted by 5% of the total input fuel energy. The experimental results indicated that the average values of brake specific fuel consumption were increased by 11.98% with the methanol-gasoline test fuel, while decreased 4.07% with the hydrogen-methanol-gasoline fuel blend. The average values of brake thermal efficiency were slightly increased by 0.07% and 0.61% with the methanol-gasoline and hydrogen-methanol-gasoline fuel blends, respectively. The combustion characteristics were improved after adding the methanol and hydrogen. In this respect, the average ignition delay values decreased by 6.45% with the addition of the methanol in gasoline, while the combustion duration, maximum cylinder pressure, and maximum heat release rate values increased by 0.85%, 1.66%, and 2.32%, respectively. Furthermore, the ignition delay and combustion duration values were substantially affected by the addition of the hydrogen and observed to be at rates of 21.10% and 6.93% reduction, respectively. In addition, the maximum cylinder pressure and maximum heat release rate values were increased by 2.02% and 4.13%. The combustion characteristics improved after the addition of hydrogen and methanol, which also considerably affected the engine performance and emission characteristics. The average values of exhaust gas temperature, hydrocarbon, carbon dioxide were increased by 0.92%, 2.26%, and 0.156% with methanol and the values of carbon monoxide and nitrogen monoxide were decreased by 1.09% and 11.82%, respectively. However, the hydrocarbon, carbon dioxide values were decreased by 13.06%, 3.90% with the hydrogen, while the carbon monoxide, exhaust gas temperature, and nitrogen monoxide were increased by 1.84%, 1.38%, and 31.68%, respectively. Although nitrogen monoxide was considerably decreased by the methanol, a drastic increase was observed with the hydrogen. The optimum brake specific fuel consumption and nitrogen monoxide values occurred between 68.61 Nm and 74.55 Nm of the engine loads for the methanol-gasoline and hydrogen-methanol-gasoline test fuels at λ = 1 and 1.2. Overall, the engine performance, combustion, and emission characteristics were improved by the addition of hydrogen and methanol in the test fuels due to their superior fuel properties compared to gasoline.

Experimental investigation of the cetane improver and bioethanol addition for the use of waste cooking oil biodiesel as an alternative fuel in diesel engines

In this study, bioethanol produced from sugar beet and biodiesel produced from waste cooking oils was blended with each other in the volumetric rates to be 20% and 80%, respectively. Cetane improver (di-tert-butyl peroxide) was added into blend fuel in 1–2–3% as volumetric. The obtained blends were used as a fuel in the diesel engine which is single-cylinder, direct-inject, four-stroke. The tests were conducted under four different engine loads (25%, 50%, 75% and 100%) and 1400 rpm engine speed. The test results showed that brake-specific fuel consumption was decreased up to 15.5% thanks to the addition of cetane improver, although biodiesel and bioethanol increased brake-specific fuel consumption by 16.1% and 27.54%, respectively. However, brake thermal efficiency values were increased up to 9.44% with both biodiesel and cetane improver added blend fuels, while brake thermal efficiency was decreased by 3.88% with bioethanol addition. The more compatible combustion characteristics with that of diesel fuel have been obtained due to especially the increase in cetane number. The use of biofuels increased both maximum cylinder pressure and heat release rate. While, with the cetane improver addition, a decrease in CO, HC and smoke opacity values was observed up to 22.5%, 17.44% and 24.44%, respectively, CO2, NOX and exhaust gas temperature values were increased up to 19.55%, 5% and 15.22%, respectively, according to bioethanol blend fuel.

Determining the effects of 2-ethylhexyl nitrate blend on isolated diesel engine attributes using the experimental and ANN approaches

ABSTRACTThis study examines the effects of 2-ethylhexyl nitrate (2EHN) blend on performance parameters of a diesel engine coated with Cr2O3 by comparing the experimental and ANN approaches. Piston, exhaust, and intake valves of the diesel engine were coated with Cr2O3 in 300-µm thickness. 2EHN was added into diesel fuel at 3%, 6%, 9% (vol.) ratios, and DE3, DE6, and DE9 blends were produced. The training and testing data of ANN were acquired from exhaust gas temperature (EGT), brake specific fuel consumption (BSFC) and engine vibration (EV) values obtained from the coated engine (CE) and uncoated engine (UE). In addition, brake thermal efficiency (BTE) was calculated and the thermal images were taken to see the coating effects more clearly. The engine tests showed that EGT, BTE, and EV values increased in CE; whereas, SFC values decreased compared to UE. In both CE and UE, the addition of 2EHN reduced SFC, but EGT, BTE, and EV values increased. Thermal images supported the changes in parameters in both CE and UE. ANN results showed that performance parameters can be estimated with low error rates. The lowest regression (R) values were obtained as 0.9939, 0.96255, and 0.99199 for EGT, SFC, and EV, respectively.

Fault Detection for Gas Turbine Hot Components Based on a Convolutional Neural Network

Gas turbine hot component failures often cause catastrophic consequences. Fault detection can improve the availability and economy of hot components. The exhaust gas temperature (EGT) profile is usually used to monitor the performance of the hot components. The EGT profile is uniform when the hot component is healthy, whereas hot component faults lead to large temperature differences between different EGT values. The EGT profile swirl under different operating and ambient conditions also cause temperature differences. Therefore, the influence of EGT profile swirl on EGT values must be eliminated. To improve the detection sensitivity, this paper develops a fault detection method for hot components based on a convolutional neural network (CNN). This paper demonstrates that a CNN can extract the information between adjacent EGT values and consider the impact of the EGT profile swirl. This paper reveals, in principle, that a CNN is a viable solution for dealing with fault detection for hot components. Based on the distribution characteristics of EGT thermocouples, the circular padding method is developed in the CNN. The sensitivity of the developed method is verified by real-world data. Moreover, the developed method is visualized in detail. The visualization results reveal that the CNN effectively considers the influence of the EGT profile swirl.

EFFECTS ON PERFORMANCE, EMISSION AND COMBUSTION PARAMETERS OF ADDITION BIODIESEL AND BIOETHANOL INTO DIESEL FUEL

In this study, safflower based biodiesel produced in the pilot plant were blended with certain amounts of diesel fuel and bioethanol from sugar beet. Diesel fuel and the blends were tested in a direct injection diesel engine and engine performance, combustion and exhaust emission characteristics were investigated. According to test results, the brake specific fuel consumptions of biodiesel blend were about 8.51% higher than diesel fuel. Bioethanol is increased brake specific fuel consumptions values up to 26.77%. The maximum cylinder gas pressure of biodiesel blend was about 0.46% higher than that of diesel fuel on average. This value was decreased about 1.75% with using of bioethanol. The exhaust emission results showed that biodiesel blend decreased carbon dioxide emissions and smoke opacity, while it increased nitrogen oxide emissions and exhaust gas temperature. Nitrogen oxide emissions, smoke opacity and exhaust gas temperature values were decreased with adding bioethanol, while it increased carbon dioxide emissions.

The Application of Artificial Neural Network in Prediction of the Performance of Spark Ignition Engine Running on Ethanol-Petrol Blends

The performance analysis of a single cylinder spark ignition engine fuelled with ethanol – petrol blends were carried out successfully at constant load conditions. E0 (Petrol), E10 (10% Ethanol, 90% Petrol), E20 (20% Ethanol, 80% Petrol) and E30 (30% Ethanol, 70% Petrol) were used as fuel. The Engine speed, mass flow rate, combustion efficiency, maximum pressure developed, brake specific fuel consumption and Exhaust gas temperature values were measured during the experiment. Using the experimental data, a Levenberg Marquardt Artificial Neural Network algorithm and Logistic sigmoid activation transfer function with a 4–10–2 model was developed to predict the brake specific fuel consumption, maximum pressure and combustion efficiency of G200 IMEX spark ignition engine using the recorded engine speed, mass flow rate, biofuels ratio and exhaust gas temperature as input variables. The performance of the Artificial Neural Network was validated by comparing the predicted data with the experimental results. The results showed that the training algorithm of Levenberg Marquardt was sufficient enough in predicting the brake specific fuel consumption, combustion pressure and combustion efficiency of the test engine. Correlation coefficient values of 0.974, 0.996 and 0.995 were obtained for brake specific fuel consumption, combustion efficiency and pressure respectively. These correlation coefficient obtained for the output parameters are very close to one (1) showing good correlation between the Artificial Neural Network predicted results and the experimental data while the Mean Square Errors were found to be very low (0.00018825 @ epoch 10 for brake specific fuel consumption, 1.0023 @ epoch 3 for combustion efficiency and 0.0013284@ epoch 5 for in-cylinder pressure). Therefore, Artificial Neural Network toolbox called up from MATLAB proved to be a useful tool for simulation of engine parameters. Artificial Neural Network model provided accurate analysis of these complex problems and has been found to be very useful for predicting the performance of the spark ignition engine. Thus, this has proved that Artificial Neural Network model could be used for predicting performance values in internal combustion engines, in this way it would be possible to conduct time and cost efficient studies instead of long experimental ones.

The Application of Artificial Neural Network in Prediction of the Performance of Spark Ignition Engine Running on Ethanol-Petrol Blends

The performance analysis of a single cylinder spark ignition engine fuelled with ethanol – petrol blends were carried out successfully at constant load conditions. E0 (Petrol), E10 (10% Ethanol, 90% Petrol), E20 (20% Ethanol, 80% Petrol) and E30 (30% Ethanol, 70% Petrol) were used as fuel. The Engine speed, mass flow rate, combustion efficiency, maximum pressure developed, brake specific fuel consumption and Exhaust gas temperature values were measured during the experiment. Using the experimental data, a Levenberg Marquardt Artificial Neural Network algorithm and Logistic sigmoid activation transfer function with a 4–10–2 model was developed to predict the brake specific fuel consumption, maximum pressure and combustion efficiency of G200 IMEX spark ignition engine using the recorded engine speed, mass flow rate, biofuels ratio and exhaust gas temperature as input variables. The performance of the Artificial Neural Network was validated by comparing the predicted data with the experimental results. The results showed that the training algorithm of Levenberg Marquardt was sufficient enough in predicting the brake specific fuel consumption, combustion pressure and combustion efficiency of the test engine. Correlation coefficient values of 0.974, 0.996 and 0.995 were obtained for brake specific fuel consumption, combustion efficiency and pressure respectively. These correlation coefficient obtained for the output parameters are very close to one (1) showing good correlation between the Artificial Neural Network predicted results and the experimental data while the Mean Square Errors were found to be very low (0.00018825 @ epoch 10 for brake specific fuel consumption, 1.0023 @ epoch 3 for combustion efficiency and 0.0013284@ epoch 5 for in-cylinder pressure). Therefore, Artificial Neural Network toolbox called up from MATLAB proved to be a useful tool for simulation of engine parameters. Artificial Neural Network model provided accurate analysis of these complex problems and has been found to be very useful for predicting the performance of the spark ignition engine. Thus, this has proved that Artificial Neural Network model could be used for predicting performance values in internal combustion engines, in this way it would be possible to conduct time and cost efficient studies instead of long experimental ones.

A Nonlinear Model Predictive Controller With Multiagent Online Optimizer for Automotive Cold-Start Hydrocarbon Emission Reduction

In this paper, a nonlinear model predictive controller (NMPC) with a multiagent heuristic optimizer, which is called dynamic particle swarm optimization (DPSO), is proposed to reduce the cold-start hydrocarbon (HC) emission of an automotive spark-ignited engine. In general, the cold-start HC emission reduction has been proven to be a very challenging control problem that has attracted increasing attention from the automotive research community. The main contribution of this paper lies in the implementation of a model-based predictive control scheme that uses a discrete nonlinear control-oriented model for calculating the control commands. In the first part of the experiments, the developed control-oriented model is validated with some data obtained through experiments. Then, by applying the controller to different cold-starting conditions, the values of exhaust gas temperature $T_{\mathrm{exh}}$ and engine-out HC emissions are controlled to reduce the cumulative tailpipe emissions $\text{HC}_{\mathrm{cum}}$ . To ascertain the veracity of the proposed control scheme, the same problem is solved using Pontryagin's minimum principle. The conducted simulations indicate the applicability of DPSO as an effective solver at the heart of the NMPC. Moreover, the results of model-in-the-loop simulation and hardware-in-the-loop testing demonstrate the acceptable performance of the proposed control scheme for real-time applications, which is based on the NMPC method for the considered problem.

An optimal learning-based controller derived from Hamiltonian function combined with a cellular searching strategy for automotive coldstart emissions

The main objective of this study is to propose a novel algorithmic framework for the implementation of a nonlinear controller for reducing the amount of tailpipe hydrocarbon emissions in automotive engines over the coldstart period. To this aim, the control problem for a given engine is formulated in the form of the standard Bolza problem, and then, the concepts of Euler–Lagrange equation and Hamiltonian function are taken into account to calculate the optimal states, co-states, and control input signals. An extreme learning machine is also linked to an experimentally validated nonlinear state-space representation of the engine during the coldstart to approximate the values of exhaust gas temperature and engine-out hydrocarbon emissions, which are two key variables for the considered control problem. To solve the resulting system of equations, a cellular variant of the particle swarm optimization technique is implemented and the existing nonlinear system of equations is solved heuristically. In addition, some constraints are exerted on the control signals to guarantee the smooth operation of the engine by applying the calculated controlling commands. Finally, the authenticity of the resulting optimal controller is validated against a classical Pontryagin’s minimum principle-based control system. Generally, the findings demonstrate the effectiveness of the proposed control methodology to reduce coldstart hydrocarbon emissions in automotive engines.

Performance and emission characteristics of diesel engine using high-viscous vegetable oil

High-viscous vegetable oil of non-edible type was used in diesel engine to test the performance and emissions. Viscosity was reduced by ethanol blending. A single cylinder four stroke DI diesel engine was employed for testing. The blends used were 90% oil with 10% ethanol (E-10), 80% oil with 20% ethanol (E-20), 70% oil with 30% ethanol (E-30), 60% oil with 40% ethanol (E-40), and 50% oil with 50% ethanol (E-50) on v–v basis. The test results of high-viscous vegetable oil-ethanol blends at full load showed 16% lower brake thermal efficiency (BTE), 33.5% higher brake specific fuel consumption (BSFC), 5.8% lower exhaust gas temperature (EGT), 1.1% higher carbon monoxide (CO), 68% higher hydro-carbon (HC), 45% lower nitrogen oxide (NO x ), and 28% higher smoke when compared to diesel operation. E-40 blend had maximum BTE (28.7%) among all blends. However, the blend E-50 resulted in maximum BSFC, HC and NO x emissions and minimum EGT, CO and smoke values at full load condition.

Applying the DOE toolkit on a Lean‐and‐Green Six Sigma Maritime‐Operation Improvement Project

PurposeThe purpose of this paper is to provide a case study on endorsing process improvement in maritime operations by implementing design of experiments on Lean Six Sigma performance responses. It is demonstrated how process efficiency and environmental muda may be dealt with simultaneously in a lean‐and‐green project driven by hardcore Six Sigma tools.Design/methodology/approachA 16‐run Taguchi‐type orthogonal design was employed to gather data for vessel speed (VS), exhaust gas temperature (EGT) and fuel consumption (FC) as modulated by a total of 15 controlling parameters synchronously. Active dependencies were inferred based on the desirability analysis method on direct process data from a performance log. This log was maintained for a long‐term monitoring during sea voyages of a double skin bulk carrier of 55,000 DWT while in sea service.FindingsA high composite desirability value was achieved eclipsing the 0.90 mark. Values well over the 0.9 level were also obtained for the three examined individual desirability values of VS, EGT and FC. Leading controlling parameters were discovered to be compressor pressure, fuel pump index, slip, governor index and MIP.Practical implicationsA Lean Six Sigma project is carried out to improve performance characteristics in ordinary maritime operations. While the company in the case study outlined in this article no longer relies on periodic inspections to determine machinery conditions, improvement on key process characteristics were nevertheless deemed worthy of ameliorating. Information retrieval from computerized continuous monitoring systems assisted in conducting experimental designs in order to obtain optimal performance. Specifically, the tuning of vessel main engine running mode was examined aiming at increasing the quality levels of output power to the shaft along with a reduction of NOx emissions.Originality/valueThis work adds an interesting paradigm in the critical field of maritime activities for processes in full gear while operating at sea. Maritime operations are an imperative necessity when expediting international trading transactions. It is the first time that such a case study has emanated from a real pilot Lean Six Sigma project which interlaces process efficiency enhancement with concurrent environmental muda reduction.

Evaluation of the relationship between exhaust gas temperature and operational parameters in CFM56-7B engines

This study presents the relationship between exhaust gas temperature (EGT) and engine operational parameters at two different power settings, including maximum continuous and take-off, in the CFM56-7B turbofan engine. The ground measurements of engine operational parameters including net thrust, fuel flow, low rotational speed, core rotational speed, pressure ratio, air temperature at engine fan inlet, take-off margin temperature, and thrust-specific fuel consumption of 51 different CFM56-7B engines are used to find the relationship mentioned in the study. This engine type is selected due to its common use by the civil aviation sector. In accordance with the results of multiple linear regression analysis, it was shown that EGT is affected by the engine operational parameters in different rate. The relationship between EGT and the operational parameters used in the maximum continuous power setting is slightly stronger than that of take-off power setting, R2=0.73 and 0.69, respectively. The fuel flow, thrust-specific fuel consumption, and take-off margin temperature are determined to be the most significant operational parameters in the correlations used to predict the EGT of 51 CFM56-7B turbofan engines in maximum continuous and take-off power settings, R2=0.28, 0.23, and 0.35 and R2=0.27, 0.14, and 0.60, respectively. It is found that there are good agreements between the predicted and measured values of EGT in the study. It can be concluded that the proposed technique is an effective tool for the EGT estimation of the turbofan engine.