Geothermal resource and reserve assessment methodology: Overview, analysis and future directions
منابع زمین گرمایی و روش ارزیابی ذخایر: بررسی اجمالی ، تجزیه و تحلیل و مسیرهای آینده-2020
Resource assessment and reserve estimation play a crucial role in the decision-making, financing, development, and operation of geothermal projects. The present study critically examines all existing resource assessment methodology and practices when quantifying power potential of geothermal fields. The potential generating capacity of geothermal projects at the early stage of development, where there is limited information about the resource, is typically estimated using the volumetric method. Sustainable operation and management of existing geothermal fields, on the other hand, rely on developing and updating a calibrated numerical reservoir model. To-date, the volumetric method and reservoir simulation remain the most appropriate tools to use for geothermal resource assessment. The former method is the recommended approach for projects that are still at the early stage of development, while the latter technique is for predicting sustainable production capacity after exploration drilling. However, building a numerical model for a project at the early due diligence stage is also useful and can complement the volumetric method. Most studies of resource assessment methodologies highlight the difficulty of obtaining accurate, predictable generating output potential. Quantification of uncertainty in predictable output is carried out using the Monte Carlo method. This review demonstrates that the probabilistic assessment using Experimental Design (ED) and Response Surface Methodology (RSM) is a more promising technique that can be easier and quicker to implement.
Keywords: Resource assessment | Resource assessment methodology | Power potential | Volumetric method | Numerical reservoir simulation | Probabilistic resource assessment
An efficient simulation optimization methodology to solve a multi-objective problem in unreliable unbalanced production lines
یک روش بهینه سازی شبیه سازی کارآمد برای حل یک مشکل چند هدف در خطوط تولید نامتوازن غیرقابل اعتماد-2019
This research develops an expert system to addresses a novel problem in the literature of buffer allo- cation and production lines. We investigate real-world unreliable unbalanced production lines where all time-based parameters are probabilistic including time between parts arrivals, processing times, time be- tween failures, repairing times, and setup times. The main contributions of the paper are a twofold. First and foremost, the mean processing times of workstations and buffer capacities, unlike the existing litera- ture, are considered as decision variables in a multi-objective optimization problem which maximizes the throughput rate and minimizes the total buffer capacities as well as the total cost of the mean process time reductions. Secondly, an efficient methodology is developed that can precisely reflect a real-world system without any unrealistic and/or restrictive assumptions on the probabilistic nature of the system, which are commonly assumed in the existing literature. One of the greatest challenges in this research is to estimate the throughput rate function since it highly depends on the random behavior of the sys- tem. Thus, a simulation optimization approach is developed based on the Design of Experiments and Re- sponse Surface Methodology to fit a regression model for throughput rate. Finally, Non-dominated Sorting Genetic Algorithm (NSGA-II) and Non-dominated Ranked Genetic Algorithm (NRGA) are used to gener- ate high-quality solutions for the aforementioned problem. This methodology is run on a real numerical case. The experimental results confirm the advantages of the proposed methodology. This methodology is an innovative expert system with a knowledge-base developed through this simulation optimization approach. This expert system can be applied to complex production line problems in large or small scale with different types of decision variables and objective functions. The application of this expert system is transformative to other manufacturing systems.
Keywords: Unreliable unbalanced production lines | Buffer allocation problem | Simulation optimization | Design of experiments | Response surface methodology | Meta-heuristics
Selection of an optimized metal oxide semiconductor sensor (MOS) array for freshness characterization of strawberry in polymer packages using response surface method (RSM)
انتخاب آرایه سنسور نیمه هادی اکسید فلزی بهینه سازی شده (MOS) برای توصیف طراوت توت فرنگی در بسته های پلیمری با استفاده از روش سطح پاسخ (RSM)-2019
An eight metal oxide semiconductor sensor (MOS) based electronic nose (e-nose) has been used to characterize freshness of strawberry in different polymer package types. Pattern recognition methods such as principal component analysis (PCA), linear discriminant analysis (LDA), and support vector machine (SVM) were used to classify and investigate the effects of polymer packages on strawberry freshness. The packages were Ethylene Vinyl Alcohol (EVOH), Polypropylene (PPP), and Polyvinyl chloride (PVC). The response surface method (RSM) was considered for selection of optimized sensor array in terms of the contribution of each sensor in sample classification. Sample headspace patterns were examined on days 1, 8 and 16. The results revealed that PCA explains 84% of the variance between the data. The LDA classified samples with an accuracy of 86.4%. The SVM method with polynomial function could accurately recognize samples as C-SVM by 86.4% and 50.6% in training and validation, and as Nu-SVM by 85.2% and 55.6% in training and validation with a radial basis function, respectively. Finally, among the eight sensors used in the study, MQ8, MQ3, TGS813, MQ4, and MQ136 sensors were selected as optimal response sensors using RSM to reduce the cost of fabrication. Furthermore, optimal application sensors for each polymer package were identified using RSM.
Keywords: Electronic nose | Strawberry | Response surface | Optimized sensors
Expert system based on a fuzzy logic model for the analysis of the sustainable livestock production dynamic system
سیستم خبره مبتنی بر یک مدل منطق فازی برای تجزیه و تحلیل سیستم پویا تولید دام پایدار-2019
This essay documents the development of an “Expert System” based on a Fuzzy Logic model, designed to analyze the outcome a number of variables have on the performance of livestock production (milk and meat) in the Huasteca region of Veracruz in order to support the decision-making of a Sustainable Livestock Production Dynamic System (SLPDS). The Expert System takes into consideration the following input variables: Temperature (T), Rain (RA), Breed (B), Health Plan Implementation (HP), Feeding Plan (FP) and Production System (PS). The aforementioned variables then have an impact on three output variables: Lactation Days (LD), Daily Milk Production (DMP) and Intervals between Births (IBB). Once the Fuzzy Logic model has been developed, an assessment of the variables is made through the Response Surface technique, which allows verifying how the variables behave in the system under study, and their impact on the output variables; as well as, testing different scenarios in order to validate the model and to identify the Livestock Production Systeḿs behavioral patterns. Through the application of Fuzzy Logic regarding the modeling of the 6 variables that impact the performance of livestock production, it is possible to capitalize on the knowledge and experience that producers have and what they have learned based on the observation and practice of many years. Therefore, being able to obtain reliable results that can be shared with agricultural producers and technicians for the improvement of the livestock productivity of the Huasteca region in the state of Veracruz. The Expert System is efficient showing an 86.67% reliability by comparing its results with a panel of specialists in livestock production. The test of the different scenarios shows interesting results when exposing the application of good livestock practices in certain conditions (temperature and rainfall) that maximize the milk and meat production of the region under study.
Keyword: Sustainable livestock production | Expert system | Fuzzy logic | Response surface
بهینه سازی ساچمه پاشی با ابزار تصمیم گیری پیچیده: تصمیم گیری چند معیاره
سال انتشار: 2018 - تعداد صفحات فایل pdf انگلیسی: 9 - تعداد صفحات فایل doc فارسی: 25
فرایند ساچمه پاشی کنترل شده با دو روش مستقل: (1) ساچمه پاشی اولیه از نوار Almen برای تعیین شدت مورد نظر، (2) ساچمه کاری نمونه واقعی با شدت Almen ، انجام شد. ساچمه پاشی Almen در مرحله اول زمان زیادی طول می کشد. که علت آن این است که علاوه بر استفاده از پارامترهای ورودی، اندازه گیری ارتفاع قوس و کنترل شدت Almen نیز باید به طور پیوسته انجام شود. نتایج به طور مستقیم بر ویژگی های واقعی موثر است. با این حال اندازه گیری ارتفاع و تنظیم پارامترهای ورودی به تکنسین کارفرما بستگی دارد. بنابراین، روش عددی با مدلسازی اجزای محدود (FEM) ، شبکه عصبی مصنوعی (ANN) و RSM (روش شناسی سطح پاسخ) پیشرفت بیشتری دارد. بدین منظور، روش عددی دیگری از جمله تصمیم گیری چند معیاره (MCDM) توسعه یافته اند و همچنین در مقایسه با نتایج آزمایشگاهی، زبری سطح و سختی سطح قرار گرفت. نتایج نشان داد که روش های MCDM با خودشان سازگار هستند و در چارچوب پارامترهای ورودی (فشار هوا، اندازه ساچمه، طول ساچمه باری) و پارامترهای خروجی نیز سازگار است (سختی سطح، زبری سطح). رویکرد Topsis نتایج سازگار را در مقایسه با دیگر رویکردها و شرایط آزمایشی ارائه می دهد.
کليدواژگان: شدت Almen | ساچمه کاری | Topsis | Vikor | GRA | MCDM
|مقاله ترجمه شده|
City-specific vehicle emission control strategies to achieve stringent emission reduction targets in Chinas Yangtze River Delta region
استراتژی های کنترل انتشار خودرو از سوی شهر برای رسیدن به اهداف دقیق کاهش انتشار در منطقه ی دلتای رودخانه یانگ تی چینی-2017
The Yangtze River Delta (YRD) region is one of the most prosperous and densely populated regions in China and is facing tremendous pressure to mitigate vehicle emissions and improve air quality. Our assessment has revealed that mitigating vehicle emissions of NOx would be more difficult than reducing the emissions of other major vehicular pollutants (e.g., CO, HC and PM2.5) in the YRD region. Even in Shanghai, where the emission control implemented are more stringent than in Jiangsu and Zhejiang, we observed little to no reduction in NOx emissions from 2000 to 2010. Emission–reduction targets for HC, NOx and PM2.5 are determined using a response surface modeling tool for better air quality. We design city-specific emission control strategies for three vehicle-populated cities in the YRD region: Shanghai and Nanjing and Wuxi in Jiangsu. Our results indicate that even if stringent emission control consisting of the Euro 6/VI standards, the limitation of vehicle population and usage, and the scrappage of older vehicles is applied, Nanjing and Wuxi will not be able to meet the NOx emissions target by 2020. Therefore, additional control measures are proposed for Nanjing and Wuxi to further mitigate NOx emissions from heavy-duty diesel vehicles.
Keywords: Vehicle | Emission control | Scenario | Yangtze River Delta
What are causes of cash flow bullwhip effect in centralized and decentralized supply chains?
علل اثرگذاری جریان پول نقد در چرخه عرضه متمرکز و غیر متمرکز چیست؟-2017
Bullwhip effect in supply chain is a phenomenon which can emerge in both inventory lev els and replenishment orders. Bullwhip effect causes variations in cash conversion cycle (CCC) across cash flow of supply chain. As a result, it can lead to inefficiencies such as cash flow bullwhip (CFB). Due to negative impact of CFB on cash flow of supply chain, it can lead to a decrease in efficiency of supply chain management (SCM). That is why sup ply chain modeling is a proper start point for effective management and control of the CFB. This paper aims to analyze concurrent impact of causes of inventory bullwhip effect and effect of their interactions on CFB based on generalized OUT policy from aspect of CCC variance. To this end, first we develop system dynamics structure of beer distribution game as simulation model which includes multi-stage supply chain under both central ized and decentralized supply chains. Then, in order to develop CFB function, we design experiments in developed simulation model using response surface methodology (RSM). Results demonstrate that if each chain member uses generalized OUT policy as replen ishment model, there still exists CFB in both chains and CFB largely stems from rationing and shortage gaming in both centralized and decentralized supply chain. In addition, when information on ordering parameters are not shared among members, parameters of down stream stage (i.e. retailer) are more important than parameters of upstream stage (i.e. man ufacturer) in reducing CFB function.
Keywords: Supply chain management | Bullwhip effect | Cash flow bullwhip |Response surface methodology
Performance of coated and uncoated mixed ceramic tools in hard turning process
عملکرد ابزار سرامیک مخلوط پوششدار و بدون پوشش در فرایند تبدیل سخت-2016
The present contribution deals with the study of the effects of cutting speed, feed rate and depth of cut on the performance of machining which traditionally named ‘‘machinability”. The focus is made on the effect of the pre-cited cutting parameters on the evolution of sur- face roughness and cutting force components during hard turning of AISI D3 cold work tool steel with CC6050 and CC650 ceramic inserts. Also, for both ceramics a comparison of theirwear evolution with time and its impact on the surface equality was proposed. The plan-Keywords:Hard turningning of experiments was based on Taguchi’s L16orthogonal array. The analysis of varianceSurface roughness Cutting forceTool wear Taguchi method RSM(ANOVA), the signal-to-noise ratio and response surface methodology (RSM) were adopted.Consequently, the validity of proposed linear regression model was checked and the most important parameter affecting the surface roughness and cutting force components were determined. Furthermore, in order to determine the levels of the cutting regime that lead to minimum surface roughness and minimum machining force the relationship between cutting factors was analyzed. The results revealed that the surface quality obtained with the coated CC6050 ceramic insert is 1.6 times better than the one obtained with uncoated CC650 ceramic insert. However, the uncoated ceramic insert was useful in reducing the machining force.© 2015 Elsevier Ltd. All rights reserved.
Keywords: Hard turning | Surface roughness | Cutting force | Tool wear | Taguchi method | RSM
Machinability evaluation and desirability function optimization of turning parameters for Cr2O3 doped zirconia toughened alumina (Cr-ZTA) cutting insert in high speed machining of steel
ارزیابی ماشینکاری و تابع مطلوبیت بهینه سازی تبدیل پارامترهای Cr2O3 به آلومینیوم نشکن زیرکونیا (کروم، ZTA) برش درج در ماشینکاری با سرعت بالا از فولاد-2016
In present study, mechanical properties, microstructure and machining parameter optimization of Cr2O3 doped zirconia toughened alumina (ZTA) ceramic insert have been investigated for application in high speed turning of AISI 4340 steel with achieving maximum tool life. The yttria stabilized zirconia (YSZ) in α-Al2O3 matrix with varying percentage of co-doped chromia (Cr2O3) is prepared to study the phase transformation behaviour. The samples are uniaxially pressed in the form of cutting inserts and subsequently sintered at 1600 1C to evaluate the mechanical properties. Hardness and fracture toughness reaches the highest value i.e. 17.40 GPa and 7.20 MPa m1/2 respectively at 0.6% Cr2O3 doped ZTA due to more metastable tetragonal ZrO2 phase present in the alumina matrix. After 50 min of machining, the ﬂank wear and surface roughness are found well below the tool rejection criteria. The cutting force also does not affect detrimentally on the job–tool interface. Turning experiments have been adopted as per central composite design (CCD) of response surface methodology (RSM) with varying 3 levels of cutting speed (140 m/ min, 280 m/min, 420 m/min), feed rate (0.12 mm/rev, 0.18 mm/rev, 0.24 mm/rev) and depth of cut (0.50 mm, 1.00 mm, 1.50 mm). The effect of each input parameter on output responses is investigated using analysis of variance (ANOVA) and modelled using regression analysis. The inﬂuence of cutting speed, feed rate and depth of cut is observed maximum for determination of ﬂank wear, cutting force and surface roughnessrespectively. Cutting speed of 420 m/min with feed rate of 0.12 mm/rev and depth of cut of 0.5 mm has been shown as optimized condition with 83.32% desirability for minimum tool failure and maximum tool life.& 2015 Elsevier Ltd and Techna Group S.r.l. All rights reserved.
Keywords: Chromia | Zirconia toughened alumina | Machinability | Central composite design | Response surface methodology
Transient probabilistic analysis for turbine blade-tip radial clearance with multi-component and multi-physics fields based on DCERSM
تجزیه و تحلیل احتمالاتی گذرا برای توربین های تیغه نوک فاصله شعاعی چند محفظه ای و چند فیزیکی مبتنی بر اساس DCERSM-2016
Against the background of the probabilistic analysis for High Pressure Turbine (HPT) Blade-tip Radial Running Clearance (BRRC) to achieve the high-performance and high-reliability of aeroengine, Distributed Collaborative Extremum Response Surface Method (DCERSM) was proposed for the dynamic probabilistic analysis of complex turbomachinery on the foundation of quadratic polynomials response surface model. On the basis of deeply investigating Extremum Response Surface Method (ERSM), the mathematical model of DCERSM was established based on quadratic polynomial function. As illustrated in BRRC transient probabilistic analysis with multiple components and multi-physics fields based on DCERSM, blade-tip radial static clearance δ = 1.82 mm is advisable synthetically considering the reliability and working efficiency of gas turbine. The reliability, distribution characteristics and failure probability of BRRC are obtained. Besides, rotational speed ω and gas temperature T are the most important factors and expansivity coefficients and surface coefficients of heat transfer show also important influence on BRRC variation. Through the comparison of three methods (DCERSM, ERSM, Monte Carlo method), it is demonstrated that DCERSM reshapes the possibility of complex turbomachinery probabilistic analysis and improves computing efficiency while preserving the accuracy. DCERSM offers a useful insight for BRRC dynamic reliability design and optimization with multi-object and multi-discipline. The efforts of this study also enrich the theory and method of mechanical reliability design.
Keywords: High pressure turbine | Blade-tip radial running clearance | Dynamic probabilistic analysis | Distributed collaborative extremum | response surface method | Multi-object multi-disciplinary