研究目的
To model and simulate an efficient pilot plant photo-chemical wastewater treatment reactor using artificial neural networks for predicting performance under different chemical and physical conditions.
研究成果
The optimized ANN model (three-layered feed-forward with trainlm algorithm, tansig and purelin transfer functions, 10 hidden neurons, calibrated at 100 iterations) accurately simulates the photochemical process with low MSE and high R2. Sensitivity analysis shows H2O2 initial concentration is the most influential parameter. This model can be used for process control and optimization in wastewater treatment.
研究不足
The study is limited to specific operational conditions and parameters; temperature was kept constant, and the model may not generalize to other pollutants or reactor designs without further training. Overfitting in ANN calibration could affect generalization accuracy.
1：Experimental Design and Method Selection:
The study employed a pilot plant jet-mixing photo-reactor for UV/H2O2 process to treat azo dye (direct red 23). Artificial neural networks (ANNs) were used for modeling, with six input variables (nozzle angle, nozzle diameter, flow-rate, irradiation time, H2O2 initial concentration, pH) and one output (treatment efficiency X). Six ANN architectures were tested with different learning algorithms and transfer functions.
2：3). Artificial neural networks (ANNs) were used for modeling, with six input variables (nozzle angle, nozzle diameter, flow-rate, irradiation time, H2O2 initial concentration, pH) and one output (treatment efficiency X). Six ANN architectures were tested with different learning algorithms and transfer functions.
Sample Selection and Data Sources:
2. Sample Selection and Data Sources: 136 experimental data sets were collected from the reactor under various conditions, normalized, randomized, and divided into training (95 sets) and testing (41 sets) portions.
3：List of Experimental Equipment and Materials:
Reactor (stainless steel vessel, 50 cm x 35 cm x 25 cm), UV-C lamps (Philips, 15 W each), nozzles, circulating pump, globe valve, rotameter, spectrophotometer (JASCO V-630), pH-meter (UB-10 Denver Instruments), reagents (direct red 23 dye, hydrogen peroxide, NaOH, H2SO4, de-ionized water).
4：Experimental Procedures and Operational Workflow:
For each run, 10 L of dye solution with H2O2 was added, pH adjusted, temperature maintained at 25°C, flow-rate set, and UV lamps turned on. Samples collected at intervals, absorbance measured, and efficiency calculated.
5：Data Analysis Methods:
MATLAB software used for ANN modeling. Mean square error (MSE) and correlation coefficient (R2) were metrics for performance evaluation. Sensitivity analysis via Connection Weight Approach to determine relative importance of input variables.
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