Difference between revisions of "Batch reactor (JuMP)"
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| − | This is an implementation of the | + | This is an implementation of the [[Batch reactor]] problem using JuMP. |
The problem was discretized and the ODEs are solved using the explicit Euler method. | The problem was discretized and the ODEs are solved using the explicit Euler method. | ||
Although not necessary in JuMP the code was divided into three parts (following AMPL) - model file, data file and run file. The run file calls the other files and performs additional tasks such as printing results. | Although not necessary in JuMP the code was divided into three parts (following AMPL) - model file, data file and run file. The run file calls the other files and performs additional tasks such as printing results. | ||
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Data file ("batchreactor_dat.jl"): | Data file ("batchreactor_dat.jl"): | ||
| − | <source> | + | <source lang="AMPL"> |
#JuMP implementation of batch reactor example using collocation | #JuMP implementation of batch reactor example using collocation | ||
#dat file | #dat file | ||
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Run file ("batchreactor_run.jl"): | Run file ("batchreactor_run.jl"): | ||
| − | <source> | + | <source lang="AMPL"> |
#JuMP implementation of batch reactor example using collocation | #JuMP implementation of batch reactor example using collocation | ||
#run file | #run file | ||
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</source> | </source> | ||
| − | [[Category: JuMP]] | + | [[Category: Julia/JuMP]] |
Latest revision as of 14:58, 19 January 2016
This is an implementation of the Batch reactor problem using JuMP. The problem was discretized and the ODEs are solved using the explicit Euler method. Although not necessary in JuMP the code was divided into three parts (following AMPL) - model file, data file and run file. The run file calls the other files and performs additional tasks such as printing results.
Model file ("batchreactor_mod.jl"):
#JuMP implementation of batch reactor example using collocation #mod file #declaring the model m = Model() #defining variables @defVar(m, x[ii=1:n_x, tt=1:N]) @defVar(m, L_control <= u[jj = 1:n_u, tt=1:N] <= U_control) #setting objective function @setObjective(m, Max, x[2,N]) #adding constraints #starting values @addConstraint(m, starting_value[ii=1:n_x], x[ii,1] == x_start[ii]) #ODE - solved with explicit euler method (i.e. x_k+1 = x_k + stepsize * f(x_k, t_k)) @defNLExpr(k_1[tt=1:N-1], 4000 * exp(-2500/u[1,tt])) @defNLExpr(k_2[tt=1:N-1], 620000 * exp(-5000/u[1,tt])) @addNLConstraint(m, ODE_nonlin[ii=1, tt=1:N-1], x[ii,tt+1] - x[ii,tt] - step_size * (-k_1[tt] * x[1,tt]^2) == 0) @addNLConstraint(m, ODE_nonlin[ii=2, tt=1:N-1], x[ii,tt+1] - x[ii,tt] - step_size * (k_1[tt] * x[1,tt]^2 - k_2[tt] * x[2,tt]) == 0)
Data file ("batchreactor_dat.jl"):
#JuMP implementation of batch reactor example using collocation #dat file #number of states n_x = 2; #number of controls n_u = 1; ##discretization #number of shooting intervals / discretization points N = 300; #starting / end time t_start = 0; t_end = 1; #time discretization step_size = (t_end - t_start)/N; #starting value x_start = [1, 0]; #bounds for control L_control = 298; U_control = 398;
Run file ("batchreactor_run.jl"):
#JuMP implementation of batch reactor example using collocation #run file using JuMP; using Ipopt; println("----------------------------------------------------") println("Time used for data") @time include("batchreactor_dat.jl") println("----------------------------------------------------") println("Time used for modeling") @time include("batchreactor_mod.jl") println("----------------------------------------------------") println("Time used for solving") @time solve(m); println("----------------------------------------------------") println("----------------------------------------------------") #printing results println("Optimal objective value is: ", getObjectiveValue(m)) println("Optimal Solution is: \n", getValue(x), getValue(u))
