Mid Semester UEB Energy Analysis of a Wastewater Treatment Plant by using Artificial Neural Network Presented by: Dr. Nor Azuana Ramli Electrical Engineering Section, Universiti Kuala Lumpur International College. UniKL Institute:

Introduction: The total electricity consumption is increasing Mid Semester UEB Introduction: The total electricity consumption is increasing The use of energy at wastewater treatment plant can be reduced by implementing energy efficiency By reducing energy consumption, electricity costs can be saved In order to achieve energy efficiency, energy management and optimization works across entire waste water treatment system UniKL Institute:

Objective of the study: The main objective in this study is to reduce the energy power consumption per cubic meter by 10 percent from current usage. (In order to achieve this objective, energy efficiency is applied by optimizing the treatment process and predicting the energy consumption)

Literature Review There are lots of successful examples showing the enormous potential of increasing energy efficiency: The Strass wastewater treatment plant in Austria, which has reached 108% of energy recovery through increasing energy efficiency. (Wett, 2007) In Central Europe, after more than 10 years of effort spent on energy auditing and benchmarking, energy consumption has been reduced by an astounding average of 38% in Switzerland and 50% in Germany (Wett et al., 2007)

Research Methodology Predictions by using ANN Energy Analysis Data Collection & Analysis Predictions by using ANN

Energy Analysis

High energy consumption equipment Proposed Optimization Measures No. High energy consumption equipment Proposed action 1 Submersible aerators Optimize the feed into each treatment train thus reducing the number of trains in operation from 6 to 4 This is turn will reduce the number of aerators operating daily from 12 to 8 2 Blowers Existing blowers are previously running on manual operation using soft starter at 50 Hz To supply and install VFD’s at 40 Hz of the blower unit To control the blower operation automatically by delay timer

Data Collection Data taken for this study are from March 2011 to February 2015 which consists of power consumption (kWh) and influent flow (m3)

The indicator used in this study is cubic meter (m3) of sewage treated per day while the energy units will be measured in kWh/m3 sewage treated per day. The current energy performance indicator (EnPI) before the analysis conducted was 0.44 kWh/m3. The target and expected reduction is about 10%. Thus, it is expected that the EnPI after the analysis is 0.40 kWh/m3. Based on an average flow of 1.3million m3/month of flow, the reduction in electrical energy usage is 1,300,000 X (0.44 – 0.40) = 52,000 kWh and estimated saving is 52,000 X RM0.337/kWh = RM 17,524/month. As the plant equipment and processes do not have any sub meter and there is no electrical meter for monitoring, the only way to verify the savings is through the Tenaga National Berhad (TNB) bills.

Comparison Method Linear Regression Linear regression is selected as a comparison with our proposed method in this analysis because it is the most common method used by industries in our country In this analysis, a scatter plot is created by using Microsoft Excel and the data taken are the active power (kWh) and influent flow (m3) Regression line is created to describe the relationship between independent and dependent variable where in this case is influent flow and the active power respectively Year 2012 is taken as our baseline and the linear equation that we obtained is used to predict the energy consumption for the next month By using Microsoft Excel, the linear regression equation that we obtained is: Comparison Method

Proposed Method Artificial Neural Network In this study, we applied Radial Basis Function (RBF) Can be employed to any sort of model either linear or nonlinear SPSS software is used to run the analysis The RBF procedure fits a radial basis function neural network, which is feedforward, supervised learning network with an input layer, a hidden layer called the radial basis function layer, and an output layer Proposed Method

Rescaling Method for Scale Dependents Network Information Input Layer Factors   Flow Number of Units 33 Hidden Layer 9 Activation Function Softmax Output Layer Dependent Variables Power 1 Rescaling Method for Scale Dependents Standardized Identity Error Function Sum of Squares

Results & Discussion

Figure 1: Comparison of plotted graph of energy consumption between linear regression and radial basis function method .

Discussion: The prediction by using RBF is better than linear regression as it produces less error where the root mean square error is 66227.13 compared to linear regression with 73392.21 The amount of cost saving from energy consumption is calculated based on the prediction results. The amount of costs from energy consumption that can be saved by using linear regression is RM 132,732 while the amount that we obtained by using RBF is RM 156,499 which is RM 23,767 difference

Electricity cost is the major expenses in operating wastewater treatment plant. In order to reduce the cost, energy consumption need to be optimized. By using the calculation of the energy usage by each equipment, we found that most of energy consumption are from blowers and aerators. To optimize the energy consumption, a few actions are taken such as proposal on optimization measures for both aerators and blowers besides prediction on energy consumption. From the results that we obtained by using RBF, the amount of production costs that can be saved is RM 156,499 which is higher than using linear regression. Conclusion…

To sum up, the objective of this study is achieved without compromise the main purpose of wastewater treatment plant which is to treat sewage in comply with the Environmental Quality Act 2009 and Malaysian Sewerage Industry Guideline. Conclusion…

Further study To obtain more accurate prediction results, methodologies from machine learning or pattern recognition can be applied for future study.

Thank You