The Experts below are selected from a list of 246 Experts worldwide ranked by ideXlab platform
Fateme Tavakoli Dastjerd - One of the best experts on this subject based on the ideXlab platform.
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Applying Relative Net Present or Relative Net Future Worth Benefit and exergy efficiency for optimum selection of a natural gas engine based CCHP system for a hotel building
Journal of Natural Gas Science and Engineering, 2016Co-Authors: Sepehr Sanaye, Mohammad Mustafa Ghafurian, Fateme Tavakoli DastjerdAbstract:Abstract Multi-objective optimization of a natural gas engine (NGE) based combined cooling, heating and power (CCHP) system for selecting the number and nominal power (NP) of NGE(s), their partial load (PL) during a year (operation strategy), heating capacity of auxiliary boiler as well as cooling capacities of absorption and electrical chillers are performed in this paper. Two objective functions were either Relative Net Present Worth Benefit (RNPWB) or Relative Net Future Worth Benefit (RNFWB) and exergy efficiency. The selection procedure was performed in two selling (excess electricity generated can be sold to the grid) and no selling (excess electricity generated cannot be sold to the grid) modes. For our case study two NGEs (with non-similar nominal powers of 1050 and 1350 kW) in selling mode and one NGE (with nominal power of 2060 kW) in no selling mode were optimum results by using either RNPWB or RNFWB and exergy efficiency. Furthermore in selling mode, with forcing a constraint (two similar NGEs), two 1100 kW NGEs were selected. Moreover by forcing another constraint (selecting one NGE) a 2300 kW NGE was selected. Results showed that selecting two non-similar NGEs (without forcing any constraint), had payback period 4.1 years. This case provided the maximum RNPWB (1013.5 × 10 4 $) or RNFWB 4233.6 × 10 4 $) and exergy efficiency (41.16%). Results of sensitivity analysis for one NGE based CCHP system with increasing equipment and unit energy costs were also reported.
Mohammad Mustafa Ghafurian - One of the best experts on this subject based on the ideXlab platform.
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New approach for estimating the cooling capacity of the absorption and compression chillers in a trigeneration system
International Journal of Refrigeration, 2018Co-Authors: Mohammad Mustafa Ghafurian, Hamid NiazmandAbstract:Abstract This paper presents a multi-objective optimization model for estimating the cooling capacity of refrigeration in a trigeneration system for both grid on and off operating modes. In trigeneration systems, the cooling demand can be provided by single effect absorption and compression chillers (as the refrigeration system). Genetic Algorithm and LINMAP selection procedure are used for obtaining the final optimum point considering four-E analysis (energy, exergy, economy and emission) with the help of net Future Worth benefit difference (NFWBD) and global exergy efficiency objective functions. It was found that the NFWBD and exergy efficiency with cooling capacity of 1800 and 5600 kW for absorption and compression chillers, respectively, are 330 × 10 6 $ and 41.9% for grid on mode, while they reduce to 232 × 10 6 $ and 32.7% for grid off mode. Sensitivity analysis in grid on mode shows that the increase in the number of equipment (gas engine and chillers) leads to the decrease in both NFWBD and exergy efficiency, while selecting two similar absorption chillers (with cooling capacity of 900 kW), has payback period of 5.1 years, and provides the maximum NFWBD (321 × 10 6 $) and exergy efficiency (40.02%).
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Applying Relative Net Present or Relative Net Future Worth Benefit and exergy efficiency for optimum selection of a natural gas engine based CCHP system for a hotel building
Journal of Natural Gas Science and Engineering, 2016Co-Authors: Sepehr Sanaye, Mohammad Mustafa Ghafurian, Fateme Tavakoli DastjerdAbstract:Abstract Multi-objective optimization of a natural gas engine (NGE) based combined cooling, heating and power (CCHP) system for selecting the number and nominal power (NP) of NGE(s), their partial load (PL) during a year (operation strategy), heating capacity of auxiliary boiler as well as cooling capacities of absorption and electrical chillers are performed in this paper. Two objective functions were either Relative Net Present Worth Benefit (RNPWB) or Relative Net Future Worth Benefit (RNFWB) and exergy efficiency. The selection procedure was performed in two selling (excess electricity generated can be sold to the grid) and no selling (excess electricity generated cannot be sold to the grid) modes. For our case study two NGEs (with non-similar nominal powers of 1050 and 1350 kW) in selling mode and one NGE (with nominal power of 2060 kW) in no selling mode were optimum results by using either RNPWB or RNFWB and exergy efficiency. Furthermore in selling mode, with forcing a constraint (two similar NGEs), two 1100 kW NGEs were selected. Moreover by forcing another constraint (selecting one NGE) a 2300 kW NGE was selected. Results showed that selecting two non-similar NGEs (without forcing any constraint), had payback period 4.1 years. This case provided the maximum RNPWB (1013.5 × 10 4 $) or RNFWB 4233.6 × 10 4 $) and exergy efficiency (41.16%). Results of sensitivity analysis for one NGE based CCHP system with increasing equipment and unit energy costs were also reported.
Sepehr Sanaye - One of the best experts on this subject based on the ideXlab platform.
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Applying Relative Net Present or Relative Net Future Worth Benefit and exergy efficiency for optimum selection of a natural gas engine based CCHP system for a hotel building
Journal of Natural Gas Science and Engineering, 2016Co-Authors: Sepehr Sanaye, Mohammad Mustafa Ghafurian, Fateme Tavakoli DastjerdAbstract:Abstract Multi-objective optimization of a natural gas engine (NGE) based combined cooling, heating and power (CCHP) system for selecting the number and nominal power (NP) of NGE(s), their partial load (PL) during a year (operation strategy), heating capacity of auxiliary boiler as well as cooling capacities of absorption and electrical chillers are performed in this paper. Two objective functions were either Relative Net Present Worth Benefit (RNPWB) or Relative Net Future Worth Benefit (RNFWB) and exergy efficiency. The selection procedure was performed in two selling (excess electricity generated can be sold to the grid) and no selling (excess electricity generated cannot be sold to the grid) modes. For our case study two NGEs (with non-similar nominal powers of 1050 and 1350 kW) in selling mode and one NGE (with nominal power of 2060 kW) in no selling mode were optimum results by using either RNPWB or RNFWB and exergy efficiency. Furthermore in selling mode, with forcing a constraint (two similar NGEs), two 1100 kW NGEs were selected. Moreover by forcing another constraint (selecting one NGE) a 2300 kW NGE was selected. Results showed that selecting two non-similar NGEs (without forcing any constraint), had payback period 4.1 years. This case provided the maximum RNPWB (1013.5 × 10 4 $) or RNFWB 4233.6 × 10 4 $) and exergy efficiency (41.16%). Results of sensitivity analysis for one NGE based CCHP system with increasing equipment and unit energy costs were also reported.
Maria C A Balatbat - One of the best experts on this subject based on the ideXlab platform.
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probabilistic dcf analysis and capital budgeting and investment a survey
The Engineering Economist, 2008Co-Authors: David G Carmichael, Maria C A BalatbatAbstract:The article surveys contributions to the literature covering the field of probabilistic discounted cash flow (DCF) analysis of individual capital investments from the earliest contributions of the 1960s to today. Such analysis includes the methods of present Worth (net present value), annual Worth, Future Worth, internal rate of return, payback period, and benefit:cost ratio. The history and development of the probabilistic case is traced, listing the main assumptions made and any restrictions to applications. The survey will be found useful by those engaged in risk management and decisions associated with investments and budgeting having uncertain outcomes.
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Probabilistic DCF Analysis and Capital Budgeting and Investment—a Survey
The Engineering Economist, 2008Co-Authors: David G Carmichael, Maria C A BalatbatAbstract:The article surveys contributions to the literature covering the field of probabilistic discounted cash flow (DCF) analysis of individual capital investments from the earliest contributions of the 1960s to today. Such analysis includes the methods of present Worth (net present value), annual Worth, Future Worth, internal rate of return, payback period, and benefit:cost ratio. The history and development of the probabilistic case is traced, listing the main assumptions made and any restrictions to applications. The survey will be found useful by those engaged in risk management and decisions associated with investments and budgeting having uncertain outcomes.
Alan M. Davis - One of the best experts on this subject based on the ideXlab platform.
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COMPSAC (2) - REFS Keynote: "Requirements for Services: Does it Make Sense?"
31st Annual International Computer Software and Applications Conference - Vol. 2 - (COMPSAC 2007), 2007Co-Authors: Alan M. DavisAbstract:Summary form only given. Since the dawn of computers, we have struggled with the best ways to record requirements for our software-based systems. Many thousands of papers have been written that describe "new" ways to discover, prune, write, interrelate, test, and manage changes to, software requirements. However, there are two trends (one historic and one Future) Worth examining more closely: (1) Historic. "Systems" and "products" have been around for many centuries before the advent of computers; requirements have only become important since computers because software has given us so much more flexibility in the features we provide to our customers. Before software, products were constructed of plastic, metal, and wood and little flexibility existed. (2) Future. With the advent of widespread broadband access to the internet, more and more companies are discovering the economies of "delivering" software not as a product but as a service. This relatively new "Software as a Service" (SOS) business model raises the issue of whether the lessons we have learned about discovering, pruning, writing, interrelating, testing, and managing changes to, software requirements still apply. But more importantly, just as software has given us incredible flexibility in the features of our products (no longer built exclusively of physical materials), now software has given us the same kind of flexibility in the features of our services (no longer based solely on human delivery). What lessons still apply? Is the business of requirements engineering unchanged? Or do new principles apply to requirements for services?
