process_simulator.evaluate_stationarity = True
ranking = [1, 1, 1]
purity_required = [0.95, 0.95, 0.95]
evaluator = ProcessEvaluator(process_simulator, purity_required, ranking)
optimization_problem = OptimizationProblem(batch_long_ternary,
evaluator,
name='case_2a',
save_log=True)
def objective_function(performance):
ranked = RankedPerformance(performance, ranking)
return -ranked.productivity * ranked.recovery * ranked.eluent_consumption
optimization_problem.objective_fun = objective_function
optimization_problem.add_variable('cycle_time', lb=10, ub=1200)
optimization_problem.add_variable('feed_duration.time', lb=10, ub=100)
optimization_problem.add_linear_constraint(
opt_vars=['feed_duration.time', 'cycle_time'], factors=[1, -1], b=0)
if __name__ == "__main__":
from CADETProcess.optimization import DEAP as Solver
opt_solver = Solver()
results = opt_solver.optimize(optimization_problem, save_results=True)
fractionation_optimizer = FractionationOptimizer(purity_required,
fractionation_objective)
evaluator = ProcessEvaluator(process_simulator, fractionation_optimizer)
optimization_problem = OptimizationProblem(batch_binary,
evaluator,
save_log=True)
def objective_function(performance):
performance = RankedPerformance(performance, ranking)
return -performance.mass * performance.recovery * performance.eluent_consumption
optimization_problem.add_objective(objective_function)
optimization_problem.add_variable('cycle_time', lb=10, ub=600)
optimization_problem.add_variable('feed_duration.time', lb=10, ub=300)
optimization_problem.add_linear_constraint(
['feed_duration.time', 'cycle_time'], [1, -1])
if __name__ == "__main__":
from CADETProcess.optimization import DEAP as Solver
opt_solver = Solver()
results = opt_solver.optimize(optimization_problem,
save_results=True,
use_multicore=False)