set exp /1*10/
    quantities /dZ1,dZ2,Z1,Z2/;

variables dZ1_mod, dZ2_mod, dZ3_mod, P1, P2, P3, P4, w;

table meas(exp,quantities)
         dZ1              dZ2                 Z1             Z2
1        -0.0690081       -0.166116384        0.79900        1.07580
2        0.2612173        -0.078451349        0.87310        0.87110
3        0.0766426        0.120056222         1.24870        0.93930
4        -0.1996174       0.038388368         1.03620        1.14680
5        -0.0100733       -0.100166287        0.74830        1.00270
6        0.2136195        0.022094978         1.00240        0.85770
7        -0.0520696       0.121377554         1.28160        1.02740
8        -0.2821079       -0.05900639         0.89440        1.13690
9        0.0268426        -0.161685119        0.78520        0.93250
10       0.8183309        0.142324196         1.15270        0.90740
;

equations E1, E2, obj;

E1(exp)..  dZ1_mod(exp) =E=  P1*meas(exp, 'Z1')*(1-meas(exp, 'Z2')) ;
E2(exp)..  dZ2_mod(exp) =E=  P2*meas(exp, 'Z2')*(meas(exp, 'Z1')-1) ;

Obj..  w =E= sum( exp, (dZ1_mod(exp) - meas(exp, 'dZ1'))*(dZ1_mod(exp) - meas(exp, 'dZ1')) + (dZ2_mod(exp) - meas(exp, 'dZ2'))*(dZ2_mod(exp) - meas(exp, 'dZ2')) );

P1.lo = 0;
P1.up = 10;
P2.lo = 0;
P2.up = 10;

 MODEL TEST / E1, E2, obj / ;

 OPTION LIMROW = 0.0;
 OPTION LIMCOL = 0.0;
*OPTION MINLP=SBB;
 TEST.optfile = 1;

 SOLVE TEST USING NLP minimiZING w;