Multi-Objective Optimization of Natural Gas Compression Power Train With Genetic Algorithms

2010 ◽  
Author(s):  
A. Hawryluk ◽  
K. K. Botros ◽  
H. Golshan ◽  
B. Huynh
Keyword(s):  
Genetic Algorithms ◽  
Energy Consumption ◽  
Gas Turbines ◽  
Load Sharing ◽  
Operating Conditions ◽  
Gas Temperature ◽  
Unit Load ◽  
Associated Gas ◽  
Multi Objective ◽  
Fan Speed

Multi-objective optimizations were conducted for a compressor station comprising two dissimilar compressor units driven by two dissimilar gas turbines, two coolers of different size, and two parallel pipeline sections to the next station. Genetic Algorithms were used in this optimization along with detailed models of the performance characteristics of gas turbines, compressors, aerial coolers, and downstream pipeline section. Essential in these models is the heat transfer between the gas and soil as it affects the pressure drop along the pipeline, and hence relates back to the coolers and compressor flow/pressure settings. Further investigative techniques were developed to refine the methodology as well as to minimize the downstream gas temperature at the suction of the next station. Current operating conditions at the station were compared to the optimized settings, showing that there is room for improving the efficiency of operation (i.e. lower energy consumption) with minimum effort on the station control strategy. Two threshold throughput conditions were determined in so far as single vs. multi-unit operations due to the dissimilarity in the compressor units and associated gas turbine drivers. The results showed that savings in the energy consumption in the order of 5–6% is achievable with slight adjustment to unit load sharing and coolers by-pass/fan speed selections. It appears that most of the savings (around 70–75%) are derived from optimizing the load sharing between the two parallel compressors, while the balance of the savings is realized from optimizing the aerial coolers settings. In particular, operating the aerial coolers at 50% fan speed (if permitted) could lead to substantial savings in electric energy consumption in some cases.

Optimization of Power Train Involving Gas Turbine Driven Compressors and Aerial Coolers

2011 ◽  
Author(s):  
K. K. Botros ◽  
H. Golshan ◽  
A. Hawryluk ◽  
B. Sloof
Keyword(s):  
Energy Consumption ◽  
Total Energy ◽  
Gas Turbines ◽  
Electrical Energy ◽  
Load Sharing ◽  
Nox Emission ◽  
Unit Load ◽  
Total Energy Consumption ◽  
Driver Gas ◽  
Parallel Pipeline

Multi-objective optimizations were conducted for a compressor station comprising two dissimilar compressor units driven by two dissimilar gas turbines, two coolers of different size, and two parallel pipeline sections to the next station. Genetic algorithms were used in this optimization along with models describing the performance characteristics of gas turbines, compressors, aerial coolers, and downstream pipeline section. Essential in these models is the heat transfer between the gas and soil as it affects the pressure drop along the pipeline, and hence relates back to the coolers and compressor flow/pressure settings. Further investigative techniques were developed to also minimize NOx and CO2e emissions along with total energy consumption, i.e. fuel (used in the driver gas turbines) and electrical energy (used in the electrical fans of the aerial coolers). Two optimization scenarios were conducted: 1) Two-objective optimization of total energy consumption and NOx emission, and 2) Two-objective optimization of total energy consumption and CO2e emission. The results showed that savings in the energy consumption in the order of 5–6% is achievable with slight adjustment to unit load sharing and coolers by-pass/fan speed selections. It appears that most of the savings (around 70–75%) are derived from optimizing the load sharing between the two parallel compressors, while the balance of the savings is realized from optimizing the aerial coolers settings. In order to optimize operation for minimum NOx emission as well, a shift towards employing more of the aerial coolers is required. Preliminary cost analysis was conducted for valuation of balancing between energy consumption vs. emission loading in terms of both NOx and CO2e.

Impact of Operating Conditions and Design Parameters on Gas Turbine Hot Section Creep Life

2010 ◽  
Author(s):  
S. Eshati ◽  
M. F. Abdul Ghafir ◽  
P. Laskaridis ◽  
Y. G. Li
Keyword(s):  
Gas Turbines ◽  
Performance Model ◽  
Operating Conditions ◽  
Creep Model ◽  
Design Parameters ◽  
Gas Temperature ◽  
Creep Life ◽  
Cooling Effectiveness ◽  
Radial Temperature ◽  
The Impact

This paper investigates the relationship between design parameters and creep life consumption of stationary gas turbines using a physics based life model. A representative thermodynamic performance model is used to simulate engine performance. The output from the performance model is used as an input to the physics based model. The model consists of blade sizing model which sizes the HPT blade using the constant nozzle method, mechanical stress model which performs the stress analysis, thermal model which performs thermal analysis by considering the radial distribution of gas temperature, and creep model which using the Larson-miller parameter to calculate the lowest blade creep life. The effect of different parameters including radial temperature distortion factor (RTDF), material properties, cooling effectiveness and turbine entry temperatures (TET) is investigated. The results show that different design parameter combined with a change in operating conditions can significantly affect the creep life of the HPT blade and the location along the span of the blade where the failure could occur. Using lower RTDF the lowest creep life is located at the lower section of the span, whereas at higher RTDF the lowest creep life is located at the upper side of the span. It also shows that at different cooling effectiveness and TET for both materials the lowest blade creep life is located between the mid and the tip of the span. The physics based model was found to be simple and useful tool to investigate the impact of the above parameters on creep life.

Sootblowing Optimization Improves Heat Rate and Enhances Operational Flexibility at Hawthorn Unit 5

2014 ◽  
Author(s):  
Neel J. Parikh ◽  
Peter Rogge ◽  
Kenneth Luebbert
Keyword(s):  
Control System ◽  
Closed Loop ◽  
Heat Rate ◽  
Operating Conditions ◽  
Operational Experience ◽  
Unit Operation ◽  
Gas Temperature ◽  
Dynamic Adjustment ◽  
Operational Parameters ◽  
Unit Load

Coal-fired units are increasingly expected to operate at varying loads while simultaneously dealing with various operational influences as well as fuel variations. Maintaining unit load availability while managing adverse effects of various operational issues such as, flue gas temperature excursions at the SCR inlet, high steam temperatures and the like presents significant challenges. Dynamic adjustment of sootblowing activities and different operational parameters is required to effectively control slagging, fouling and achieve reliability in unit operation. Closed-loop optimizers aim to reduce ongoing manual adjustments by control operators and provide consistency in unit operation. Such optimizers are typically computer software-based and work by interfacing an algorithmic and/or artificial intelligence based decision making system to plant control system [1]. KCP&L is in the process of implementing Siemens SPPA-P3000 combustion and sootblowing optimizers at several Units. The Sootblowing Optimizer solution determines the need for sootblowing based on dynamic plant operating conditions, equipment availability and plant operational drivers. The system then generates sootblower activation signals for propagation in a closed-loop manner to the existing sootblower control system at ‘optimal’ times. SPPA-P3000 Sootblowing Optimizer has been successfully installed at Hawthorn Unit 5, a 594-MW, wall-fired boiler, firing 100 percent Powder River Basin coal. This paper discusses implementation approach as well as operational experience with the Sootblowing Optimizer and presents longer-term operational trends showing unit load sustainability and heat rate improvement.

scholarly journals Reliable simple zonal method of the furnace thermal calculation

2005 ◽  
Vol 9 (2) ◽  
pp. 45-55
Author(s):  
Vladan Ivanovic
Keyword(s):  
Energy Consumption ◽  
Flue Gas ◽  
Thermal Load ◽  
Operating Conditions ◽  
Thermal Calculation ◽  
Gas Temperature ◽  
Zonal Method ◽  
One Dimensional ◽  
Furnace Performance ◽  
Steam Boilers

The calculation of the furnace in the industrial and power boilers is the most important and the most responsible part of the thermal calculation, and it has important influence on the rationalization of energy consumption. In the paper one-dimensional zonal method of the furnace thermal calculation of steam boilers is presented. It can successfully define disposition of flue gas temperature and specific thermal load of screen walls with height of the furnace in case of uneven deposits distribution which vary in size and quality. Its greatest use is for comparing furnace performance under various operating conditions.

Experimental and Computational Analysis of Additive Manufactured Augmented Backside Liner Cooling Surfaces for Use in Micro Gas Turbines

2020 ◽  
Author(s):  
Adamos Adamou ◽  
Colin Copeland
Keyword(s):  
Heat Transfer ◽  
Film Cooling ◽  
Gas Turbines ◽  
Conjugate Heat Transfer ◽  
Reverse Flow ◽  
Operating Conditions ◽  
Experimental Results ◽  
Gas Temperature ◽  
Cfd Models ◽  
High Heat Transfer

Abstract Augmented backside cooling refers to the enhancement of the backside convection of a combustor liner using extended heat transfer surfaces to fully utilise the cooling air by maximising the heat transfer to pumping ratio characteristic. Although film cooling has and still is widely used in the gas turbine industry, augmented backside cooling has been in development for decades now. The reason for this, is to reduce the amount of air used for liner cooling and to also reduce the emissions caused by using film cooling in the primary zones. In the case of micro gas turbines, emissions are of even greater importance, since the regulations for such engines will most likely become stricter in the following years due to a global effort to reduce emission. Furthermore, the liners investigated in this paper are for a 10 kWe micro turbine, destine for various potential markets, such as combine heat and power for houses, EV hybrids and even small UAVs. The majority of these markets require long service intervals, which in turn requires the combustor liners to be under the least amount of thermal stress possible. The desire to also increase combustor inlet temperatures with the use of recuperated exhaust gases, which in turn increase the overall system efficiency, limits the cooling effectiveness of the inlet air. Due to all these reasons, an advanced form of augmented backside cooling would be of substantial significance in such a system. Currently some very simple designs are used in the form of straight plain fins, transverse strips or other similar geometries, but the creation of high heat transfer efficiency surfaces in such small sizes becomes very difficult with traditional subtractive manufacturing methods. When using additive manufacturing though these types of surfaces are not an issue. This paper covers the comparison of experimental results with conjugate heat transfer CFD models and empirical heat balance models for two different AM liner cooling geometries and an AM blank liner. The two cooling fin geometries include a rotating plain fin and an offset strip fin. The liners were tested in an AM built reverse flow radial swirl stabilised combustion chamber at a variety of operating conditions. During the experiments the surfaces were compared using a thermal camera to record the outer liner temperature which was viewed through a quartz outer casing. The experimental results showed that the cooling surfaces were effective at reducing the liner temperatures with minimal pressure losses for multiple operating points. Those results were then compared against the conjugate heat transfer CFD models and the empirical calculations used to design the surfaces initially. From this comparison, it was noticed both the CFD and empirical calculations under predicted the wall temperatures. This is thought to be due to inaccuracies in the predicted flame temperatures and the assumed emissivity values used to calibrate the thermal imaging camera. Further uncertainties arise from the assumption of a constant air and hot gas temperature and mass flow along the cooling surfaces and the lack of data for the surface roughness of the parts.

Multi Objective Optimization of Electro Discharge Machining of Resin Bonded Silicon Carbide

2011 ◽  
Vol 110-116 ◽  
pp. 1556-1560
Author(s):  
R. Venkataraman
Keyword(s):  
Silicon Carbide ◽  
Gas Turbines ◽  
Operating Conditions ◽  
Machining Process ◽  
Pareto Optimal ◽  
Electrode Wear ◽  
Pareto Optimal Front ◽  
Multi Objective ◽  
Electro Discharge Machining ◽  
Pulse On Time

This work is aimed at optimizing the various parameters of the electro discharge machining process in order to Maximize material removal rate (MRR) and Minimize electrode wear rate (EWR) for machining silicon or resin bonded silicon carbide, which is widely used in various applications like high-temperature gas turbines, bearings, seals and linings of industrial furnaces. The five parameters being optimized are intensity supplied by the generator of the EDM machine, open voltage, pulse on time, duty cycle and pressure of flushing fluid. The polynomial models for MRR and EWR proposed by Luis, Puertas and Villa [1] in terms of the five input parameters was used for formation of the objective function. Optimization was carried out using the multi objective genetic algorithm, which is a heuristic search technique that mimics natural selection. A Pareto-optimal front was obtained using this technique, and the points lying on this front represent the set of optimal solutions for the optimization problem. The resultant Pareto– optimal front can be used to select the appropriate operating conditions depending on the specific MRR, EWR or combination requirements.

A Comparative Analysis of Turbine Rotor Inlet Temperature Models

2011 ◽  
Author(s):  
Cristhian Maravilla Herrera ◽  
Sergiy Yepifanov ◽  
Igor Loboda
Keyword(s):  
Gas Turbines ◽  
Thermal State ◽  
Turbine Rotor ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Inlet Temperature ◽  
Gas Temperature ◽  
Thermal Boundary Conditions ◽  
Power Setting ◽  
Stress Estimation

Life usage algorithms constitute one of the principal components of gas turbine engines monitoring systems. These algorithms aim to determine the remaining useful life of gas turbines based on temperature and stress estimation in critical hot part elements. Knowing temperatures around these elements is therefore very important. This paper deals with blades and disks of a high pressure turbine (HPT). In order to monitor their thermal state, it is necessary to set thermal boundary conditions. The main parameter to determine is the total gas temperature in relative motion at the inlet of HPT blades Tw*. We propose to calculate this unmeasured temperature as a function of measured gas path variables using gas path thermodynamics. Five models with different thermodynamic relations to calculate the temperature Tw* are proposed and compared. All temperature models include some unmeasured parameters that are presented as polynomial functions of a measured power setting variable. A nonlinear thermodynamic model is used to calculate the unknown coefficients included in the polynomials and to validate the models considering the influence of engine deterioration and operating conditions. In the validation stage, the polynomial’s inadequacy and the errors caused by the measurement inaccuracy are analyzed. Finally, the gas temperature models are compared using the criterion of total accuracy and the best model is selected.

Early fault detection of gas turbine hot components under different ambient and operating conditions

2021 ◽  
pp. 095441002098689
Author(s):  
Jiao Liu ◽  
Jinfu Liu ◽  
Daren Yu ◽  
Zhongqi Wang ◽  
Weizhong Yan ◽  
...  
Keyword(s):  
Fault Detection ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Detection Method ◽  
Operating Conditions ◽  
Exhaust Gas ◽  
Gas Temperature ◽  
Spatial Correlations ◽  
Early Fault Detection ◽  
Exhaust Gas Temperature

Failure of hot components in gas turbines often causes catastrophic results. Early fault detection can prevent serious incidents and improve the availability. A novel early fault detection method of hot components is proposed in this article. Exhaust gas temperature is usually used as the indicator to detect the fault in the hot components, which is measured by several exhaust thermocouples with uniform distribution at the turbine exhaust section. The healthy hot components cause uniform exhaust gas temperature (EGT) profile, whereas the hot component faults could cause the uneven EGT profile. However, the temperature differences between different thermocouple readings are also affected by different ambient and operating conditions, and it sometimes has a greater influence on EGT than the faults. In this article, an accurate EGT model is presented to eliminate the influence of different ambient and operating conditions on EGT. Especially, the EGT profile swirl under different ambient and operating conditions is also included by considering the information of the thermocouples’ spatial correlations and the EGT profile swirl angle. Based on the developed EGT model, the detection performance of early fault detection of hot components in gas turbine is improved. The accuracy and effectiveness of the developed early fault detection method are evaluated by the real-world gas turbine data.

scholarly journals Multi-objective optimization of microfiltration of baker’s yeast using genetic algorithm

2017 ◽  
pp. 211-220 ◽  
Author(s):  
Natasa Lukic ◽  
Marija Bozin-Dakic ◽  
Jovana Grahovac ◽  
Jelena Dodic ◽  
Aleksandar Jokic
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Energy Consumption ◽  
Flow Rate ◽  
Transmembrane Pressure ◽  
Feed Flow Rate ◽  
Multi Objective Optimization ◽  
Permeate Flux ◽  
Multi Objective ◽  
Reduction Of Energy Consumption

This paper presents a multi-objective optimization model by applying genetic algorithm in order to search for optimal operating parameters of microfiltration of baker?s yeast in the presence of static mixer as a turbulence promoter. The operating variables were the suspension concentration, transmembrane pressure, and feed flow rate. Two conflicting objective functions, maximizing the permeate flux and maximizing the reduction of energy consumption, were considered. This multi-objective optimization problem was solved by using the elitist non-dominated sorting genetic algorithm in the Matlab R2015b software. The Pareto fronts along with the process decision variables correspondding to the optimal solutions were obtained. It was found that lower suspension concentrations (2-4.5 g/L), feed flow rate in the range 109-127 L/h, and transmembrane pressure of 1 bar were the optimal process parameters which yielded maximum permeate flux (177-191 L/(m2h)) and maximum reduction of energy consumption (44-50%). Finally, the results were compared with the previously published results obtained by applying desirability function approach. Given that genetic algorithms have generated multiple solutions in a single optimization run, the study proved that genetic algorithms are preferable to classical optimization methods.

Decarbonization Advancements in Pressure Pumping With Gas Turbines

2021 ◽  
Author(s):  
Andrea Mantini ◽  
Steven Goldstein ◽  
Colleen Rimlinger
Keyword(s):  
Gas Turbine ◽  
Gas Turbines ◽  
Cost Savings ◽  
Operating Conditions ◽  
Emissions Reduction ◽  
Dual Fuel ◽  
Permian Basin ◽  
Fuel Blend ◽  
Industry Studies ◽  
Associated Gas

Abstract Key changes have triggered the push for frac fleet innovation. With environmental regulation efforts to cut down on emissions increasing, more and more companies are transitioning to the use of electric fleet equipment. Electric fleets use natural gas, which burns cleaner than diesel fuel. Our study found the gas turbine outperformed Tier 4 dual fuel blend (DF) reciprocating engines and demonstrated a step change improvement in both direct and indirect emissions reductions over the 20+ year lifecycle of the Baker Hughes LM2500 in Permian and Williston Basins’ field operating conditions. An even greater impact to direct GHG (as CO2 equivalent) emissions reduction came to light when the potential to reduce flaring of associated gas was considered. Gas turbines have been proven to have the best-in-class emissions for powering pressure pumping fleets and lead the industry on fuel cost savings and in achieving commitments to reduce carbon emissions in places like the Permian Basin in Texas and remote areas across the world. Though, recent industry studies abominably suggest that Tier 4 diesel and Tier 4 dual fuel (DF) engine technologies offer an alternative with emissions benefits in comparison to current gas turbine offerings this study demonstrate the contrary.

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