Temperature–Salinity Coupled Compensation for Conductance-Based Water-Cut Measurement Using a Bayesian-Regularized Backpropagation Neural Network

Conductance-based water-cut measurements are sensitive to coupled changes in temperature and salinity. This study develops a Bayesian-regularized backpropagation neural network (BR-BPNN) for compensation using the mean sensor peak voltage, temperature, and salinity as inputs. A grid of 198 measured calibration points was used to construct a trilinear response surface and generate 9000 derived samples for developing BR-BPNN, a ridge-regularized nonlinear basis-expansion baseline (MPR), and support vector regression (SVR). On 40 independently measured test conditions spanning 55–95% nominal water cut, the fitted BR-BPNN achieved a mean absolute error of 1.72 percentage points, compared with 3.52 for MPR and 2.32 for SVR under the reported model-selection procedures. Training-strategy comparisons and physical-plane holdouts further assessed interpolation-based training. The selected 3-10-5-1 network was deployed on an STM32L496RGT3 and evaluated with newly acquired sensor inputs under the same 40 nominal conditions. Embedded evaluation achieved a mean absolute error of 1.868 percentage points and a mean network forward-inference time of 52.05 μs. The results demonstrate a compact implementation of coupled compensation for embedded conductance-based water-cut measurement within the tested laboratory domain.