Development of an Intelligent Gas Recognizer for Analysis of Sewer Gas

Transcription

1 Development of an Intelligent Gas Recognizer for Analysis of Sewer Gas Prof. Paramartha Dutta Dept. Computer & System Sciences Visva-Bharati University Santiniketan West Bengal

2 Introduction Objective Composition of Sewer Gas Death Statistics Safety Limit Gas Analysis System GC Method Chemical Analysis Method Alternative Solution System Overview Sensor Array DAS Gas Mixture Analysis System Portable Setup Mixture Analysis System Outline

3 Contd.. Cross-Sensitivity Intelligent Approach Data Collection Process Data Preprocessing Training Pattern BPN Approach Neuro Genetic Approach Neuro Swarm Approach Neuro Simulated Annealing Approach Results Conclusion

4 Introduction Our attention is on design issues of Intelligent Recognizer for Detecting Proportion of Components Present in Manhole Gas Mixture. Manholes are outlet-inlet points built across sewer pipeline network. Sewer pipeline network is used to drain out domestic waste products of cities and towns. Many hazardous gases are formed due to decomposition of these waste products into Sewer Pipeline. The mixture of these poisonous gases is known as manhole gas mixture. We are using Search algorithm for training of neural network for detecting proportion of gases present in manhole gas mixture.

5 Objectives Collection and analysis of the sewer gas. Sensing the individual gas components in the manhole gas mixture. Design an intelligent electronics circuit for detecting the presence of any hazardous gas component crossing Lower Explosive Limit (LEL) and raising alarm in that situation.

6 Composition of Sewer Gas Sewer gas is a complex mixture of toxic and non-toxic gases produced and collected in sewage systems by the decomposition of organic household or industrial wastes. The major components of sewer gas can include: nitrogen (N 2 ), hydrogen sulfide (H 2 S), carbon dioxide (CO 2 ), methane (CH 4 ), ammonia (NH 3 ), biological organisms, water vapor, and other chemicals discharged to the effluent stream.

7 Death Statistics SL. NO DATE PLACE DEATH REPORTED BY 1 NOV 26, 2011 JALANDHAR 1 TIMES OF INDIA 2 NOV 19, 2011 NEW DELHI, 3 HINDUSTAN TIMES 3 FEB 07, 2011 LUDHIANA 2 TIMES OF INDIA 4 DEC 08, 2010 JURONG EAST, SINGAPORE 1 RACHEL CHAN 5 MAY 20, 2010 MUMBAI 2 NDTV 6 NOV 15, 2008 BANGALORE 3 TIMES OF INDIA 7 MAR 28, 2003 NEW DELHI 2 TIMES OF INDIA

8 Safety Limits GAS GENERATION PROCESS SAFE LIMIT CH4 Decomposing Hydrocarbon < 10,000 ppm H2S Stagnant of sewer for long time < 600 ppm NH3 Biological wastage < 25 ppm CO Oxidation of Hydrocarbon and lack of oxygen present < 40 ppm CO2 Oxidation of Hydrocarbon when sufficient oxygen present < 10,000 ppm

9 Gas Analysis System There are two conventional existing methods for gas analysis 1. Gas Chromatography Method 2. Chemical Analysis Method

10 Gas Chromatography Method Manhole at PHOOLBAGAN Gas found 1 st peak N 2 (47.1%) 2 nd peak CH 4 (52%) 3rd peak CO 2 (0.9%) Manhole at ULTADANGA Gas found N 2 (only found) Calibration file Peak Gas found 1 st peak N 2 (14.7%) 2 nd peak CH 4 (81%) 3rd peak CO 2 (4.3%)

11 Disadvantages of GC Method Gas Chromatography machine interfaced with computer is a sophisticated unit, so not possible to use in field. Cylinder of carrier gas is required for GC. Calibration with known gas sample is first step in GC method. After that the unknown sample is injected which gives result in comparison with the calibrated one. That makes a big ambiguity because the result shows the presence or absence of only those gas which are used in calibration process and it is a fact that the manhole contains variety of gases. The TCD type GC machine cannot detect gases which are having same thermal conductivity distinctly.

12 Suction Flow meter 1 LPM for 1 hour Chemical Analysis Method Charcoal Tube ORSAT Apparatus Sausage tube Hand Pump Collection of Carbon Monoxide CO Rubber bag Gas collected in Sausage tube Collection of Methane (CH4) Collection & analysis of Carbon Dioxide (CO2) by ORSAT apparatus CO analyzer in Lab Absorbed in charcoal tube GC in Lab Gas collected in rubber bag Pass through 30% KOH

13 Process of Gas Collection & Analysis System Contd.. Collection of Ammonia Hydrogen Sulfide & Nitrogen Oxide Inside the Manhole How we have collected the Gas Gas passed through Absorbing Solution 0.9 LPM for 1 Hour Titration in Lab Gas Absorbing Solution Ammonia H 2 SO 4 (1N) Hydrogen Sulfide ZnSO 4 Nitrogen Oxide NaOH + Na 3 As

14 Chemical Analysis Results (ppm) Gas Sample 1 Sample 2 Sample 3 Sample 4 Sample 5 NO x H 2 S NH CH CO CO

15 Disadvantages of Chemical Analysis Method Lots of dangerous chemicals have to carry in the field. The chemical handling is also a big problem in the field without experts. The sampling process takes 1.5 hours in field, and near about 7 days required in lab for analysis. By then precious lives are lost. Inside the hand sampler the gas may be diluted due to the presence of air inside the wolf bottle. Minimum one chemist and two trained peoples are required only for sampling the gas. We are not getting the on spot analysis which is the most important part of this project.

16 Alternative Solution To overcome the problems of GC and Chemical method we have designed an electronic gadget which is capable to detect the presence of any hazardous gas inside the manhole. If the presence of the gas is Very low than toxic limit Green LED Near to toxic level Yellow LED Above toxic limit Red LED + Buzzer The gadget have two basic parts Sensor Array DAS

17 Sensor Array D A S The Complete System Overview Electronic Hardware X Gas Mixture X X Computer ( Intelligent System) Reporting And Comparison X X First Module Second Module Third Module

18 Contd..

19 Sensor Array Consist with five different sensors for five individual gases procured from China & USA. China sensors are basically Semiconductor type: MP-4 (Methane Sensor) MQ-7 (Carbon monoxide Sensor) MQ-136 (Hydrogen Sulfide Sensor) MQ-137 (Ammonia Sensor) Posifa ( from USA) sensor is MEMS TCD (Thermal Conductivity Detector) type sensor used for carbon dioxide gas.

20 A Comparative Study Of The Procured Sensors Features MQ-4 MQ-7 MQ 136 MQ 137 MEMS,TCD Manufacturer Winsen, China Winsen China Winsen,China Winsen,China Posifa, USA Target Gas CH4 CO H2S NH3 CH 4, CO 2 High Selectivity Sensitivity to gas (R air/ R gas) Detection Range >5 >5 >3 > ppm (0.03%-1%) for CH ppm for CO ppm for H2S ppm for NH3 0-5% - CO2 0-40% - CH4 Supply voltage/ current 5V+/-0.2V 5V+/-0.2V 5V+/-0.2V 5V+/-0.2V 16 ma Power Consumption <900mW 350mW <900mW <900mW 4mW (Pulse) 40mW (CC) Heating resistance 80+/-10 Ω 31+/-3 Ω 31+/-3 Ω 31+/-3 Ω 150 Ω Fast response and recovery Cost $ $90 (Freight cost) 10s/ 30s 150s/ 150s 30s/ 60s 30s/ 60s $ 1.5 $ $ $ 20 + $50 (Freight cost)

21 Sensor Array With DAS

22 Data Acquisition System ARDUINO- UNO Consists of a 8-bit Atmel AVR microcontroller 5 volt linear regulator and a 16 MHz crystal oscillator. RS-232 serial connection for interfacing Consists of 6 analog I/O pins with facility of simultaneous data processing

23 DAS operational procedure

24 Gas Mixture And Analysis System in Laboratory Portable Test Setup Gas Mixture Analysis System

25 Portable Test Setup The discrete gas sensors for gas CO, CO 2, H 2 S, NH 3 and CH 4 have been imported and a portable test setup with all this sensors has been set in laboratory. Portable Test Setup Portable Test Setup with Computer Interface

26 Setup for Toxic Gas Mixture Analysis

27 Contd..

28 Gas Analysis Results We have analyzed the sensors in various gas concentrations. The methane sensor (MP 4) has been analyzed in various ppm levels (from 0 ppm to 5000 ppm). The result is shown as in figure. The sensor response is exponentially decaying as the concentration of Methane gas is increasing.

29 The Cross-Sensitivity Concept: If a sensor say S which is built to sense only one target gas say X can also show sensitivity to any other gas say Y, so the sensitivity of sensor S to gas Y is the cross sensitivity of sensor S towards gas Y.

30 For cross sensitivity checking we have used all the five sensors methane, CO, CO2, H2S, NH3. The gas used here is LPG. The curve shows that all the sensors are sensing the LPG gas, but the sensor resistance of methane sensor changes maximum. The Carbon monoxide sensor, Hydrogen sulfide sensor and Ammonia sensor are also showing some sensor responses. Though an ideal sensor should not response in any other gas except the target gas. This sensing property is called Cross Sensitivity. Contd..

31 Contd.. Sensors Gas SH 2 S SCO 2 SCH 4 SNH 3 SNO 2 SCO H 2 S CO CH NH NO SO

32 Intelligent approaches Three Layered Back propagation Neural Network Approach (BPN Approach) Genetically Trained Neural Network Approach (Neuro Genetic Approach) Neural Network Trained using Particle Swarm Optimization (Neuro Swarm Approach) Neural Network Trained using Simulated Annealing Algorithm (Neuro SA Approach)

33 Data Collection Process Study safety limits of hazardous gases in manhole gas mixture. Several samples of gas mixture in known concentration are prepared. Samples are prepared by taking concentration of gases which should vary around their safety limits. Each mixture sample is passed over the surface of semiconductor based gas sensor array. Sensor responses are noted in a tabular form.

34 Synthetic Data Sample Sample Mixed gas (ppm) Sensor response NH 3 CO H 2 S NO 2 CH 4 NH 3 CO H 2 S NO 2 CH

35 Data Preprocessing Importance of normalization. Normalization of mixture sample values. NCsi = Csi / Cmax NCsi : Normalized concentration. Csi : Concentration of the gas itself. Cmax : Maximum concentration among all the samples Normalization of sensor response values. NRsi = Rsi / Rmax NRsi : Normalized sensor response. Rsi : Sensors individual response. Rmax : Maximum response among all the samples.

36 Network Training Pattern Input Vector Sensor responses are the input to the system, thus input vector I is formed from the sensor responses. I = [0.1699, , , , ] Target Vector Reporting concentration of gases present in the given gas mixture is the system output, thus target vector T is formed from the gas mixture. T = [0.01, 0.02, 0.02, 0.02, 0.4]

37 Input Output BPN Approach I 1 H 1 O 1 I2 H2 O2 In Hn On Input Layer Hidden Layer Output Layer In O 1 Input node that hold i/p Value but do not perform any computation Neural node with sigmoidal Activation function Synaptic links

38 Backpropagation Algorithm A neural network is a massively parallel distributed processor combination that has a natural propensity for storing experiential knowledge and making it available for us. Backpropagation algorithm is a form of supervised learning for multilayer neural networks, also known as the generalized delta rule. Error data at the output layer is back propagated to earlier ones, allowing incoming weights to these layers to be updated. 38

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40 Performance Converges 150 th Iteration Betterment of SSE Best SSE At N/W Configuration SSE SSE Series Serie Iteration (Multiple of 100) Performance Analysis based on SSE vs. Iteration No. of Hidden Nodes Performance Analysis based on n/w Configuration 40

41 Neuro Genetic Approach We are using the real valued genetic algorithm for training of the neural network. The soft computing tools such as neural network and genetic algorithm are coupled in such a manner that it forms a concept of hybrid neuro genetic algorithm. The neuro genetic algorithm is offering a minimization problem where it tries to minimize the sum of squared error induced by the neural network. Genetic algorithm is search algorithm based on the dynamics of natural selection and natural genetics.

42 Contd..

43 Performance SSE Converges Betterment at of 200 th iteration performance Performance w r t Hidden Nodes Best SSE for the Configuration SSE 0.15 SSE 0.03 Series Iteration (Multiple of 100) Number of Hidden Nodes Performance Analysis based on SSE vs. Iteration Performance Analysis based on n/w Configuration 43

44 Neuro Swarm Approach In the Neuro Swarm Optimization Algorithm as the name implies we are using particle swarm optimization technique to search out the optimal synaptic weights combination for which the neural network produce minimum Sum Squared Error on presenting input vectors/patterns. Particle Swarm Optimization is search algorithm inspired by the behavior of swarm. Eventually the swarm as a whole, like a flock of birds collectively foraging for food, is likely to move close to an optimum of the fitness function.

45 Contd..

46 SSE SSE Performance Analysis Converges Betterment of at 150SSE th No further Improvement Iteration (multiple of 100) Best SSE found at N/W Configuration Algorithm Performance against Iteration Number of Hidden Nodes Performance Analysis based on N/W Configuration 46

47 Simulated Annealing Algorithm Simulated annealing (SA) is a probabilistic algorithm for locating a good approximation to the global optimum of a given function in a vast search space. It is suitable for global optimization. Simulated Annealing is a variant of local (neighborhood) search. A local search algorithm always moves in a direction of improvement (e.g. hill climbing algorithm, which often has tendency of getting stuck at local optimum solution). SA overcomes this disadvantage of local search by accommodating non-improving moves to avoid getting stuck at a local optimum 47

48 Contd..

49 Performance Performance Analysis based on N/W Configuration

50 Performance Comparison

51 Performance Comparison

52 Performance Comparison

53 Performance Comparison

54 Performance Comparison

55 Results Systems Output/Results are presented in concentration (PPM) of each gas component present in the given gas mixture test sample. Network operates on normalized data. So its outputs are normalized value, which needs to be denormalized to covert normalized value to PPM. Denormalization System Output = Network Output * Cmax Cmax:Maximum concentration among all the samples 55

56 Results Contd.. SL No. (N/W Node No.) I/P Normalized Value of Sample 2 of Sample Data Table (Normalized Sensor Response) N/W Actual Output System s Output (PPM) Actual Concentr ation (PPM)

57 Conclusion Proposed system Provides a solution to multiple gas detection issue. Article Addresses the vital cross-sensitivity issue. Article Proposes a simple Intelligent system for detection gas mixture using different Intelligent techniques. Analyzing the performance of the developed system. Reporting the detected gas in terms of concentration (PPM) of that gas in the mixture 57

58 Relevant Publication BOOK CHAPTER: JOURNALS: Varun Kumar Ojha and Paramartha Dutta. Performance Comparison of Different Intelligent Techniques Applied on Detecting Proportion of Different Component in Manhole Gas Mixture, Handbook of Research on Computational Intelligence for Engineering, Science and Business, IGI Global, USA, to publish. Varun Kumar Ojha, Paramartha Dutta, and Hiranmay Saha. Performance Analysis Of Neuro Genetic Algorithm Applied On Detecting Proportion Of Components In Manhole Gas Mixture, International Journal of Artificial Intelligence & Application, July 2012, Accepted. Varun Kumar Ojha and Paramartha Dutta. Performance Analysis of Neuro Swarm Optimization Algorithm Applied on Detecting Proportion of Components in Manhole Gas Mixture, Artificial Intelligence Research, Accepted Published in September INETRNATIONAL CONFERENCES: Varun Kumar Ojha, Paramartha Dutta, Hiranmay Saha, and Sugato Ghosh. Detection of proportion of different gas components present in manhole gas mixture using backpropagation neural network. In Intentional Conference on Information & Network Technology (ICINT 2011), Chennai, India, April IACSIT 2012, Vol-37 pp ISBN Varun Kumar Ojha, Paramartha Dutta, Hiranmay Saha, and Sugato Ghosh. Application of Real Valued Neuro Genetic Algorithm in Detection of Components Present in Manhole Gas Mixture. In Proceedings of The Second International Conference On Computer Science, Engineering And Application (Springer 2012), Delhi, India, May Vol-1, pp , ISSN Varun Kumar Ojha, Paramartha Dutta, Hiranmay Saha, and Sugato Ghosh. A Neuro-Swarm Technique for the Detection of Proportion of Components in Manhole Gas Mixture. In Proceedings Of International Conference On Modeling, Optimization And Computing (ICMOC 2012), Kanyakumari, India, April 2012 Vol-2, pp Varun Kumar Ojha, Paramartha Dutta, Hiranmay Saha, and Sugato Ghosh. Linear Regression Based Statistical Approach For Detecting Proportion Of Component Gases In Manhole Gas Mixture. In International Symposium on Physics and Technology of Sensors (IEEE 2012), Pune, India, March 2012, Accepted & Presented. Sugato Ghosh, Animesh Roy, Sarat Singh, Varun Kumar Ojha, Paramartha Dutta, Hiranmay Saha. Sensor Array for Manhole Gas Analysis. In International Symposium on Physics and Technology of Sensors (IEEE 2012), Pune, India, March 2012, Accepted & Presented. Varun Kumar Ojha, Paramartha Dutta, Hiranmay Saha, and Sugato Ghosh. A Novel Neuro Simulated Annealing Algorithm for Detecting Proportion of Component Gases in Manhole Gas Mixture. In Proceedings Of International Conference On Advances In Computing And Communications (ICACC 2012) IEEE, Kochi, India, August 2012, Accepted.

59 Thank You