(177e) Advanced Modeling for the Operation Conditions Optimization in a Wastewater Treatment Pilot Plant, Using the Response Surface Methodology (RSM) and the Artificial Neural Networks (ANN)

Summary

In this paper is analyzed the performance of two process equipment where the
most significant changes occur in the quality of treated water, from a pilot
wastewater treatment plant (WWTP) in a cans manufacturing process plant.

   In the first stage of this treatment process, coagulation and flocculation
have been used to remove total hardness and total suspended solids,
primarily.   

In a second step of this treatment process, a reverse osmosis (RO) system has
been used to remove total dissolved solids (TDS) and silicon oxide mainly.
This plant which manufactures cans for beverage packaging, is located south of
the Mexican Republic.

1.
Introduction

In this research, the studied wastewater was generated by a can making plant
and its raw water contains significant quantities of TDS, suspended solids and
grease. It leads to high values of conductivity, hardness, silicon oxide, and
COD; for these reasons this wastewater must be treated before its discharge to
water bodies or reuse the treated-wastewater, leading to increase the water
reuse rate.

A pilot scale plant with a capacity of 0.9084 m³/h (4.0 gallons per
minute, gpm) was built to treat the studied industrial effluents. Its flowchart
is shown in Figure 1.

Figure 1. Flowchart of our wastewater treatment pilot
plant

2.
Experiment

Before reaching the coagulating reactor, the flow of industrial wastewater
receives the injection of some chemicals. For our experiments were used three
different coagulants (CaO, Al₂(SO₄)₃ and FeCl₃)
and two different flocculants (NALCO 9907 and NALCO 3249). Several tests were
conducted in our jar equipment test.

To achieve optimum performance of this equipment, tests were carried out in a
settling column built for laboratory tests scale.  This is the best way to
determine the percentage of suspended solids removal from water samples.

Before the RO membrane, the wastewater flow receives the injection of a pair of
chemicals to ensure optimal operation. A design of experiments was carried out
at pilot plant level, with different combinations of pH (HCl 30%) and different
concentrations of anti-fouling Viatec 4000 in the wastewater. Solid deposits on
the RO membrane were chemically analyzed.

Once selected the experiment with best performance, the software ?Multifactor
RSM from Design Expert 8.0? and the software ?MATLAB R2009b applying the
artificial neural network tool box, ANN? were applied.

3. Results and discussions

     The best results in jars testing were obtained
in case 2 at pH (5, 10), stirring speed during the addition of the coagulant
(250 RPM during 10 minutes), stirring speed during the addition of the
flocculant (100 RPM during 45 seconds) and temperature (23 ^oC), with
the combination of Al₂(SO₄)₃ as a coagulant
and NALCO 9907 as a flocculant.

Sedimentation tests at laboratory scale, using the sedimentation column
provided enough experimental data of the recovered solids at different times
and different depths. With this information were plotted isopercentage curves,
which show the solid removal efficiency over time in the settling tank of our
WWT pilot plant. 

      According with the design of experiments carried out in the
RO membrane process, we performed 9 different runs, with 6 continuous days of
operation in every one, collecting operational data every hour (pH, antifouling
concentration, SiO₂ concentration, SDT concentration, permeated
water flow, etc.,) from every run. With this information collected, we
identified the run with the best performance (quality characteristics and
mainly the best permeated water flow recovered). For this best performance
case, we used the response surface methodology (RSM) and artificial neural
networks (ANN) in order to identify the optimal operation conditions.

The chemical composition of solids
deposited on the RO membrane is mostly silicon oxide, with traces of sodium,
magnesium, aluminum and calcium.

4. Conclusions

Ø      According with the results of our ?Design of Experiments?, performed
with the information obtained in Jar test at laboratory level, it is concluded
that the pair of coagulant and flocculant Al₂(SO₄)₃-NALCO
9907 is the one that obtained the best results in turbidity.

Ø      In relation to the isopercentage curves, made in the
sedimentation practice at laboratory scale, is expected a 100% of settleable
solids removal. This is a function of the 60 minutes of residence time in each
one of the equipments, the coagulating reactor and settler.

Ø      The best results in the RO membrane experiments, was
the case 9 with pH=4, antifouling concentration in 3 mg/L, inlet water flow in
4 gpm, inlet SiO₂ concentration in 19 mg/L and inlet TDS
concentration in 1346 mg/L. It was achieved a permeated water flow of 2.85 gpm,
which represents a 71.25% of water
recovery with sufficient quality to be reused (reduction of 94.72% of total dissolved
solids and 79% of SiO₂). 

Ø      Increasing the pH of the industrial waste water fed
to the reverse osmosis membrane, the permeated water flow decrease at any concentration
of anti-fouling.

Ø      An increment of anti-fouling concentration causes an
increase of permeated water flow.

Ø      The permeated water flow is maintained at 3 gpm
during the first 89 hours in case 9.

Ø      The WWT pilot plant shows much better operational
results (treated water quality and mainly the total recuperated water flow)
than the obtained by Reynolds and Richards (the original technology being used
at the Toluca can manufacturing plant).

Ø      In the response surface analysis, the factors that
were significant in the permeated water flow, are the pH and anti-fouling
concentration with a correlation coefficient R² = 0.9749

Ø      In the stages of training, validation and testing of
the neural network, for each case were obtained correlation coefficients R²
greater than 0.99

Ø      According to chemical analysis performed random in
three sections of the reverse osmosis membrane, it is considered that the chemical
composition of the solids deposited is silicon oxide with traces of sodium,
magnesium and aluminum. In some deposits were found traces of carbon, which may
come from residues of organic compounds.

Ø      The evolution of ?mean squared error? of modeling, using
neural networks for case 9, decreases from a value slightly greater than 1 to
0.001 in 49 epochs.

Ø      The total dissolved solids concentration in the
permeated water flow, change from 85 to 55 mg/L in a time range of 140 hours,
averaging about 70 mg/L.

Ø      The case 9, carried out in the reverse osmosis
membrane, is the best one in performance due to the higher permeated water flow
reached and lower concentrations of silicon oxide and total dissolved solids at
the outlet.