I am a beginner in julia coding and trying to optimize an objective function that consits of normal CDF perecentage.
I tried to do this in several ways but it didn't work. In this version of code I put STD = to constant becuase I keep getting the error
LoadError: ArgumentError: Normal: the condition σ > zero(σ) is not satisfied. Even when I define all xs to be >1 or adding a constriant for STD.
Also when specifying STD as constant I still get error, and this model can't be solved.

The code is as follows:
m=Model(solver=IpoptSolver())
@variable(m,x1>=0)
@variable(m,x2>=0)
@variable(m,x3>=0)

function mue(x1,x2,x3)
5x1+15x2+2x3
end
function std(x1,x2,x3)
 x1+2x2+5x3
end

function dist(x1,x2,x3)
 Normal(mue(x1,x2,x3),5)
end

function cpro(x,y)
cdf(x,y)
end


JuMP.register(:dist, 3, dist, autodiff=true)
JuMP.register(:cpro, 2, cpro, autodiff=true)

@constraint(m, std(x1,x2,x3)>=1)
@NLobjective(m, Max, (cpro(dist(x1,x2,x3),130) -cpro(dist(x1,x2,x3),70)*100)


print(m)
status = solve(m)

println("objective value: ", getobjectivevalue(m))
println("x1: ", getvalue(x1))
println("x2: ", getvalue(x2))
println("x2: ", getvalue(x3))


The error I get currently is as follows:

LoadError: TypeError: eval_f: in typeassert, expected Float64, got Distributions.Normal{Float64}
 in eval_f(::JuMP.UserFunctionEvaluator, ::SubArray{Float64,1,Array{Float64,1},Tuple{UnitRange{Int64}},true}) at C:\Users\*\.julia\v0.5\JuMP\src\nlp.jl:1324
 in forward_eval(::Array{Float64,1}, ::Array{Float64,1}, ::Array{ReverseDiffSparse.NodeData,1}, ::SparseMatrixCSC{Bool,Int64}, ::Array{Float64,1}, ::Array{Float64,1}, ::Array{Float64,1}, ::Array{Float64,1}, ::Array{Float64,1}, ::Array{Float64,1}) at C:\Users\nourma\.julia\v0.5\ReverseDiffSparse\src\forward.jl:128
 in forward_eval_all(::JuMP.NLPEvaluator, ::Array{Float64,1}) at C:\Users\*\.julia\v0.5\JuMP\src\nlp.jl:435
 in eval_grad_f(::JuMP.NLPEvaluator, ::Array{Float64,1}, ::Array{Float64,1}) at C:\Users\*\.julia\v0.5\JuMP\src\nlp.jl:488
 in initialize(::JuMP.NLPEvaluator, ::Array{Symbol,1}) at C:\Users\*\.julia\v0.5\JuMP\src\nlp.jl:394
 in loadproblem!(::Ipopt.IpoptMathProgModel, ::Int64, ::Int64, ::Array{Float64,1}, ::Array{Float64,1}, ::Array{Float64,1}, ::Array{Float64,1}, ::Symbol, ::JuMP.NLPEvaluator) at C:\Users\nourma\.julia\v0.5\Ipopt\src\IpoptSolverInterface.jl:44
 in _buildInternalModel_nlp(::JuMP.Model, ::JuMP.ProblemTraits) at C:\Users\*\.julia\v0.5\JuMP\src\nlp.jl:1219
 in #build#93(::Bool, ::Bool, ::JuMP.ProblemTraits, ::Function, ::JuMP.Model) at C:\Users\*\.julia\v0.5\JuMP\src\solvers.jl:314
 in (::JuMP.#kw##build)(::Array{Any,1}, ::JuMP.#build, ::JuMP.Model) at .\<missing>:0
 in #solve#88(::Bool, ::Bool, ::Bool, ::Array{Any,1}, ::Function, ::JuMP.Model) at C:\Users\*\.julia\v0.5\JuMP\src\solvers.jl:134
 in solve(::JuMP.Model) at C:\Users\*\.julia\v0.5\JuMP\src\solvers.jl:116
 in include_string(::String, ::String) at .\loading.jl:441
 in include_string(::Module, ::String, ::String) at C:\Users\*\.julia\v0.5\CodeTools\src\eval.jl:32
 in (::Atom.##54#57{String,String})() at C:\Users\*\.julia\v0.5\Atom\src\eval.jl:79
 in withpath(::Atom.##54#57{String,String}, ::String) at C:\Users\*\.julia\v0.5\CodeTools\src\utils.jl:30
 in withpath(::Function, ::String) at C:\Users\*\.julia\v0.5\Atom\src\eval.jl:45
 in macro expansion at C:\Users\*\.julia\v0.5\Atom\src\eval.jl:77 [inlined]
 in (::Atom.##53#56{String,String})() at .\task.jl:60
while loading C:\Users\*\Desktop\Julia codes\nonlinear exp, in expression starting on line 33