Working Fluid Design for Organic Rankine Cycle

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Working Fluid Design for Organic Rankine Cycle ( working-fluid-design-organic-rankine-cycle )

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2-2 Continuous-Molecular Targeting approach CAMD 9 ^􏰌􏰋􏰓 􏰍􏰎􏰋􏰢 􏰃􏰍􏰎􏰌􏰇􏰎􏰖􏰍􏰖􏰈 d􏰙􏰊􏰑􏰋􏰌􏰇􏰎􏰑 􏰆􏰋􏰈􏰇􏰊􏰋􏰗 W􏰊􏰍􏰉􏰋􏰈􏰈 W􏰋􏰊􏰔􏰍􏰊􏰕􏰙􏰎􏰉􏰋 􏰏􏰋􏰐􏰑􏰐 􏰋􏰔􏰔􏰇􏰉􏰇􏰋􏰎􏰉􏰒􏰟 􏰉􏰍􏰈􏰌􏰈􏰟 􏰓􏰍􏰠􏰋􏰊 􏰍􏰖􏰌􏰓􏰖􏰌􏰘 WZK􏰃􏰂^^ 􏰄E􏰆 tKZh/􏰆 􏰆􏰂^/'E 􏰃􏰍􏰎􏰌􏰇􏰎􏰖􏰍􏰖􏰈 􏰆􏰋􏰉􏰇􏰈􏰇􏰍􏰎􏰈 􏰏􏰋􏰐􏰑􏰐 K􏰓􏰋􏰊􏰙􏰌􏰇􏰎􏰑 􏰃􏰍􏰎􏰗􏰇􏰌􏰇􏰍􏰎􏰈􏰟 t􏰍􏰊􏰝􏰇􏰎􏰑 &􏰛􏰖􏰇􏰗 W􏰊􏰍􏰓􏰋􏰊􏰌􏰇􏰋􏰈 􏰘 K􏰓􏰌􏰇􏰕􏰙􏰛 W􏰊􏰍􏰉􏰋􏰈􏰈 W􏰋􏰊􏰔􏰍􏰊􏰕􏰙􏰎􏰉􏰋 􏰆􏰋􏰈􏰇􏰑􏰎􏰋􏰗 􏰃􏰍􏰕􏰓􏰍􏰎􏰋􏰎􏰌􏰈 􏰏􏰋􏰐􏰑􏰐 􏰊􏰙􏰠 􏰕􏰙􏰌􏰋􏰊􏰇􏰙􏰛􏰈􏰟 D^􏰄􏰡􏰈􏰘 ^􏰌􏰋􏰓 􏰌􏰠􏰍􏰢 ^􏰌􏰊􏰖􏰉􏰌􏰖􏰊􏰋 D􏰙􏰓􏰓􏰇􏰎􏰑 '􏰇􏰚􏰋􏰎 􏰈􏰋􏰌 􏰍􏰔 􏰕􏰍􏰛􏰋􏰉􏰖􏰛􏰙􏰊 􏰜􏰖􏰇􏰛􏰗􏰇􏰎􏰑 􏰜􏰛􏰍􏰉􏰝􏰈 􏰌􏰍 􏰜􏰋 􏰈􏰉􏰊􏰋􏰋􏰎􏰋􏰗 􏰙􏰎􏰗 􏰓􏰞􏰒􏰈􏰇􏰉􏰙􏰛 􏰓􏰊􏰍􏰓􏰋􏰊􏰌􏰒 􏰌􏰙􏰊􏰑􏰋􏰌􏰈 &􏰛􏰖􏰇􏰗 􏰓􏰙􏰊􏰙􏰕􏰋􏰌􏰋􏰊􏰈 􏰔􏰍􏰊 K􏰓􏰌􏰇􏰕􏰙􏰛 ^􏰒􏰈􏰌􏰋􏰕 W􏰋􏰊􏰔􏰍􏰊􏰕􏰙􏰎􏰉􏰋 DK>􏰂􏰃h>􏰄Z 􏰆􏰂^/'E 􏰆􏰇􏰈􏰉􏰊􏰋􏰌􏰋 􏰆􏰋􏰉􏰇􏰈􏰇􏰍􏰎􏰈 􏰏􏰋􏰐􏰑􏰐 d􏰒􏰓􏰋 􏰍􏰔 􏰃􏰍􏰕􏰓􏰍􏰖􏰎􏰗􏰘 Figure 2-2: CoMT-CAMD problem formulation for integrated working fluid and system design (Adapted from Bardow et. al.[16]) maxx,y f (x) s.t. h[x,g ̃]=0 g ̃ − g ( x , y ) = 0 c(x, y) ≤ 0 (2-1) (2-2) ( 2 - 3 ) (2-4) where x and y denote the process variables and the pure component fluid parameters re- spectively. Equation (2-1) refers to the design objective of the ORC system. The objective function can be either the efficiency of the complete system or the output power depending on the application. Commercially, ORC systems, as introduced in Chapter 1, are widely used as waste heat recovery systems in industries, conversion of geothermal heat, solar thermal systems for which fuel is not consumed directly and hence power output from the cycle be- comes the main priority as the maximum efficiency point may not coincide with the maximum power point. In such cases, the objective function is defined as the power output. In ORC systems such as those used for biomass conversion, the objective function is defined as the net efficiency as the economic feasibility of such system depends directly on the fuel costs. In case of a multi-objective optimization problem, the total system cost can also be an objective for the optimization in which the solutions are those which do not dominate each other (also called non-dominated or Pareto-optimal solution). The selection of the final solution among optimum points located on the Pareto frontier requires a process of decision-making. In fact, this process is mostly carried out based on engineering experiences and importance of each objective for decision-maker [24]. Equation (2-2) represents the constraints applied to the system (x) and the fluid thermo- dynamic quantities (g ̃). The thermodynamic properties of the fluid are computed from models g(x, y) represented by (2-3). The process and fluid inequality constraints are summarized in (2-4). Master of Science Thesis Akshay Hattiangadi

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