Abstract:
This Study explores the application of Artificial Neural Networks (ANN) to enhance
wastewater treatment processes at Kakira Sugar's Effluent Treatment Plant (ETP). Leveraging
three years of historical data combined with daily monitoring of key operational
parameters—including FlowRate, pH, Total Suspended Solids (TSS), temperature, Total
Dissolved Solids (TDS), and Electrical Conductivity (EC)—the study developed an ANN
model capable of accurately predicting effluent Chemical Oxygen Demand (COD) levels.
The model was constructed using MATLAB’s Neural Network Toolbox and trained with a 5fold
cross-validation
approach,
ensuring
robust
generalizability
and
minimizing
overfitting
through
iterative
weight
adjustments
via
the Levenberg-Marquardt
algorithm.
Performance
metrics
such
as
Mean
Squared
Error
(MSE), Root
Mean
Squared
Error
(RMSE),
and
the
coefficient
of
determination
(R²)
were
used
to evaluate
the
model's accuracy,
revealing
strong
predictive
performance
on
training
data
and
highlighting
areas
for
improvement
in validation
and
testing
phases.
Sensitivity
analysis
using
Monte
Carlo
Simulation
(MCS)
further
identified
FlowRate,
pH,
and
temperature
as
the most
influential
factors
affecting
COD
predictions.
The
outcomes
of this
project
underscore
the potential
of integrating
ANN
into
wastewater
treatment
operations,
offering
a
promising
tool
for
real-time
process
optimization,
regulatory
compliance,
and
enhanced
resource
management.
Future
research
is recommended
to
expand
datasets,
refine
model generalization,
and
explore
hybrid
modelling
approaches
to
further
elevate
the
predictive
capabilities
and
operational
efficiency
of wastewater
treatment
systems.
