  Within integrated tokamak plasma modelling, turbulent transport codes are
typically the computational bottleneck limiting their routine use outside of
post-discharge analysis. Neural network (NN) surrogates have been used to
accelerate these calculations while retaining the desired accuracy of the
physics-based models. This paper extends a previous NN model, known as
QLKNN-hyper-10D, by incorporating the impact of impurities, plasma rotation and
magnetic equilibrium effects. This is achieved by adding a light impurity
fractional density ($n_{imp,light} / n_e$) and its normalized gradient, the
normalized pressure gradient ($\alpha$), the toroidal Mach number ($M_{tor}$)
and the normalized toroidal flow velocity gradient. The input space was sampled
based on experimental data from the JET tokamak to avoid the curse of
dimensionality. The resulting networks, named QLKNN-jetexp-15D, show good
agreement with the original QuaLiKiz model, both by comparing individual
transport quantity predictions as well as comparing its impact within the
integrated model, JINTRAC. The profile-averaged RMS of the integrated modelling
simulations is <10% for each of the 5 scenarios tested. This is non-trivial
given the potential numerical instabilities present within the highly nonlinear
system of equations governing plasma transport, especially considering the
novel addition of momentum flux predictions to the model proposed here. An
evaluation of all 25 NN output quantities at one radial location takes
$\sim$0.1 ms, $10^4$ times faster than the original QuaLiKiz model. Within the
JINTRAC integrated modelling tests performed in this study, using
QLKNN-jetexp-15D resulted in a speed increase of only 60 - 100 as other physics
modules outside of turbulent transport become the bottleneck.
