I want to fit 3 data sets to a model consisting of 3 differential equations and 7 parameters. I want to find the parameters best fitting to my model.

I have searched for several related examples. I borrowed this：https://mathematica.stackexchange.com/questions/28461/how-to-fit-3-data-sets-to-a-model-of-4-differential-equations

```
sol = ParametricNDSolveValue({Sg'(t) == -((kg Sg (t) X(t))/(
Ksg + Sg(t))), Sg(0) == 490,
Sc'(t) == -((kc Sc (t) Sg(t) X(t))/((Ksc + Sc(t)) (Ksg + Sg(t)))),
Sc(0) == 230,
X'(t) == -b X(t) - (
kc Sc(t) Sg(t) X(t))/((Ksc + Sc(t)) (Ksg + Sg(t)) T) + (
kg Sg(t) X(t) Y)/(Ksg + Sg(t)), X(0) == 22}, {Sg, Sc, X}, {t, 0,
100}, {kg, Ksg, kc, Ksc, b, T, Y});
abscissae = {0., 18., 30., 45., 58., 64., 68., 73., 78., 83.5, 90.5,
95., 99.};
ordinates = {{490.18, 467.06, 442.16, 420.82, 322.32, 248.67, 209.15,
161.54, 98.73, 28.71, 5.34, 0.76, 0.31
}, {231.3, 232.8, 209.1, 167.1, 127.3, 100.0, 87.5, 76.8, 52.8,
52.7, 57.7, 57.0, 58.5}, {22, 30, 36, 60, 77, 92, 107, 115, 125,
138, 151, 156, 156}};
data = ordinates;
ListLinePlot(data, DataRange -> {0, 100}, PlotRange -> All,
AxesOrigin -> {0, 0})
transformedData = {ConstantArray(Range@Length(ordinates),
Length(abscissae)) // Transpose,
ConstantArray(abscissae, Length(ordinates)), data}~
Flatten~{{2, 3}, {1}};
model(kg_, Ksg_, kc_, Ksc_, b_, T_, Y_)(i_, t_) :=
Through(sol(kg, Ksg, kc, Ksc, b, T, Y)(t), List)((i)) /;
And @@ NumericQ /@ {kg, Ksg, kc, Ksc, b, T, Y, i, t};
fit = NonlinearModelFit(transformedData,
model(kg, Ksg, kc, Ksc, b, T, Y)(i, t), {kg, Ksg, kc, Ksc, b, T,
Y}, {i, t}, Method -> "Gradient");
Show(Plot(Evaluate(Table(fit(i, t), {i, 3})), {t, 0, 100},
PlotLegends -> {Sg, Sc, X}),
ListPlot(data, DataRange -> {0, 100}, PlotRange -> All,
AxesOrigin -> {0, 0}))
```

But it’s not working well. This is the first time I use MMA for fitting. I would really appreciate your help!