# simple-flowsheet.in
#
# Example to show how to hand-build a flowsheet object. The resulting
# flowsheet can be optimized using the optimize method in the
# Flowsheet class coupled with the appropriate optimization object. In
# this example, we use a genetic algorithm for optimizing the flowsheet.
#
# Copyright (c) 2000, Eric S Fraga, All rights reserved
#
# -----------------------------------------------------
#
# tell the system where to find the classes referenced below
use jacaranda.design.ps		# for base stream and component classes
use jacaranda.design.units	# for the unit models and unit variables
use jacaranda.design.visual	# for visualization tool
use jacaranda.util.opt          # for genetic algorithm optimisation

#
# create the two components first
#
variables
 variable Base 10
end
KistaComponent propane
	base "45.36/Base * kmol/hr"
	bp 230.8
	cpv -4.224 3.062e-1 -1.586e-4 3.214e-8
	lhtc "0.583 *kW/m^2/K"
	vhtc "5.0 *kW/m^2/K"
end
KistaComponent nbutane
	base "226.8/Base * kmol/hr"
	bp 272.4
	cpv 9.487 3.313e-1 -1.108e-4 -2.821e-9
	lhtc "0.583 *kW/m^2/K"
	vhtc "5.0 *kW/m^2/K"
end
#
# now create the feed stream which will be liquid at 1 atm pressure
# (default) and temperature chosen automatically to be the bubble
# point temperature for that pressure.  We also specify that we want
# the enthalpy states to be discretized using 32 levels.
#
Phase phase
	comps propane nbutane
	x 0.35 0.65
	flow "100 * kmol/hr"
	phase liquid
end
PStream feed
	add phase
	nstates 32
end
#
# we have one real unit in the flowsheet: the distillation unit which
# separates propane from n-butane, running at 10 atm with a recovery of
# 99% for both top and bottom products. The operating pressure is
# constrained to be in the range [1,30] and 8 discrete values are
# requested within that range. The discrete values are spread
# uniformly on a log basis. Note, however, that the flowsheet defined
# below works in continuous space so these discrete values are
# actually not used.
#
Distillation D1
  LogReal P "10 * atm" "1 *atm" "30 *atm" 8
  Real rec 0.99
  Component key propane		# light key component
end
#
# now describe the units which provide the feed stream and which
# accept the products from the distillation unit
#
# the feed tank has the feed stream defined above
#
FeedTank Feed
  Stream feed feed
end
#
# two product tanks, one which accepts streams with 90% propane and
# the other with 90% or better n-butane.
#
ProductTank PropaneTank
  Expression spec "x[propane]>0.90" # 90% purity of propane in stream
end
ProductTank NButaneTank
  Expression spec "x[nbutane]>0.90"
end
#
# lastly, we must define the utilities that are available to meet the
# heating and cooling requirements of the flowsheet.  These come from
# Rathore et al, AIChE J, 1974.
#
DiscreteUtilities utils
    hot  "Steam @ 28.23 atm"	503.5 503.5 "5000 *W/m^2/K" "1.0246 / GJ"
    hot  "Steam@11.22atm"	457.6 457.6 "5000 *W/m^2/K" "0.773824 / GJ"
    hot  "Steam@4.08atm"	417.0 417.0 "5000 *W/m^2/K" "0.573203 / GJ"
    hot  "Steam@1.70atm"	388.2 388.2 "5000 *W/m^2/K" "0.41796 / GJ"
    cold  "ColdWater@32.2degC"  305.2 305.2 "500 *W/m^2/K" "0.0668737 / GJ"
    cold  "Ammonia@1degC"	274.00 274.00 "500 *W/m^2/K" "1.65035 / GJ"
    cold  "Ammonia@-17.68degC" 	255.32 255.32 "500 *W/m^2/K" "2.96871 / GJ"
    cold  "Ammonia@-21.67degC"	251.33 251.33 "500 *W/m^2/K" "3.96226 / GJ"
end
#
# now set the global data based on the objects defined above
#
Data
	utils utils
end

# finally, build up a flowsheet which will be manipulated later using
# a genetic algorithm
#
Flowsheet flowsheet
	build
		Feed D1
		D1 PropaneTank NButaneTank
	end
	continuous		# specify continuous optimization
end

# create the optimizer object that will be used by the flowsheet
# object defined below. this optimizer is based on a genetic algorithm
# but could be any other sort (check out uk.ac.ucl.che.esf.util.opt.* for some
# alternatives or build your own!). the genetic algorithm expects
# certain parameters, most of which are defined in the associated
# Population object
#
# crossover	rate, between 0 and 1
# elite		number of solutions to keep from one generation to the
#		next
# generations	number of generations to perform
# mutation	rate, between 0 and 1
# output	level of output desired: 0=none, 1=little, 2=running
# 		stats, >3=verbose to the extreme
#
# and also expects a population object that will 
# population	size of population
VariableEncodedChromosome var
  function flowsheet
end
RouletteSelection roulette
end
Population pop
   chromosome var       # variable encoded chromosome
   crossover 0.7	# crossover rate (70%)
   elite 1		# elite set
   mutation 0.1		# mutation rate (10% -- quite high)
   n 20			# population size
   selection roulette   # use Roulette wheel fitness based selection
end
GA ga
   outputlevel 3	# generate a lot of output
   ngen 50		# evolve over 50 generations
   printfrequency 5	# print every 5
   population pop	# the population object
   evolve		# solve the problem by evolution
   print		# and generate some output
end