Intelligent classification of ammonia concentration based on odor profile

This thesis presents the intelligent classification of ammonia concentration based on the standard of oil and gas industries wastewater discharge. The intelligent classification using signal processing is a well-known technique in many applications and as well in the oil and gas industry. The intelligent classification technique for ammonia concentration classification is a demanding technique especially in the environmental sector. Ammonia solution properties and ammonia solution preparations were studied in this thesis which commonly used in industry. The objectives of this thesis are to develop an intelligence classification of ammonia concentration based on the oil and gas industry wastewater discharge schedule and to analyze performance of the intelligent classification of ammonia concentration based on the oil and gas industry wastewater discharge schedule. In this thesis the ammonia odor profile has been pre-identified by chemist using four sensor array. The ammonia concentration was validated using a commercialized gas sensor and spectrophotometer to cross-validated e-nose instrument. The odor profile from two different samples; high (20 ppm and 25 ppm) and low (5 ppm, 10 ppm and 1 5ppm) concentration that have been normalized and visualized in a 2D plot to extract the unique patterns. The variance of the low and high concentration of ammonia odor profile has been identified as different group samples. This group samples have been analyzed statistically using Boxplot, calibration curve and proximity matrix, The thesis describes the statistical techniques to visualize the pattern and using mean features to classify between the low and high concentration. Two intelligent classification techniques have been used which are Artificial Neural Network (ANN) using the back-propagation approaches and then, the result of ANN model was cross-validated.using CBR. Both ANN model and CBR classifier have been measured using several performance measures. From the results, it is observed that ANN model and CBR classifier are capable of classifying 100% of ammonia concentration odor profile from the water. The results can also significantly reduce the cost and time, and improve product reliability and customer confidence

Classification for ammonia in water by specific concentration using artificial neural network (ANN)

Water pollution caused by poor management of waste water release or dump need to be monitored. This paper present to monitor on ammonia release by industry which can caused death to plant worker. This monitoring was a combination between E-Nose and Classification techniques which is ANN. ANN the most common retrieval method that used in industry nowadays. Furthermore, ANN classification successful to classify 100% accuracy for specific concentration of Ammonia which is using Lavernberg-Marquardt (LM) algorithm with supervised learning and fast convergence Back Propagation (BP) method

The classification of meat odor-profile using K-nearest neighbors (KNN)

Meat is a type of food that humans consume and it is an important part of their diet. In recent years, there are several cases involving meat product fraud have come to public attention. There have been numerous reports that meat labelled, certified or sold as halal may not be and that some butchers in the market mix beef and pork meat. This is causing problems for customers, particularly Muslim customers. Meat can be distinguishhed using human sensors such as vision and smell. The limitation is that meat alterations cannot be clearly distinguished by visual evaluation. In addition, unreliable reliance on the human nose to detect odor is highly risky and hazardous to human health. Electronic Nose (Enose) was proposed in this study in order to work as well as a human sensor that is made up of four Metal Oxide Sensor (MOS) gas sensors to collect the raw data from the beef and pork meat samples. The raw data were then pre-processed and the data was extracted using the mean feature to produce the odor-profile. Finally, the K-Nearest Neighbors (KNN) method was used to classify the data. KNN was then evaluated using a performance measure. As a result, the classification using KNN has 99.24 % highest accuracy at training and testing ratio 70:30 using weight K=1 at Euclidean distance and all rules

The study of raw water based on quality parameter using smell-print sensing device

Water is a renewable natural resource and comprises about 70% of earth whilst the balance is land. Cleanliness and purity of drinking water is important for human health worldwide, thus it is important to know the water body source content so that consumption of it does not give any risk to human body’s health. This study focuses on establishing a case library profile and classification of water based on recommended by Ministry of Health (MOH). This study water quality parameters such as iron (Fe) and pH is obtained using Electronic nose (E-nose). E-nose is an instrument that mimics human nose that has the ability to sniff in advance for volatile odor. However, colourless and odourless chemical usually undetectable by normal eyes or noses. Case Based Reasoning (CBR) is used in performing the intelligent classification that involved CBR computation, voting and performance measure. The similarity result shows that the technique accomplished to classify with 97.5% accuracy, 88.0% specificity and 92.2% accuracy

Lubricant oil odor-profile classification using case based reasoning intelligent classification method

Lubricant Oil is one of the products from the crude petroleum refinery process. The implementation of lubricating oil automotive sector is very crucial to make sure the smoothness of moving parts in the vehicles engine. The smoothness of engine of vehicles influence the performance of vehicle at the highest level. Common method used by public to determine the aging level of lubricant oil is by checking the mileage meter mounted on the vehicles dashboard. In the world of research, researchers used various methods and instruments such as ICP-MS, AAS and so on. However, these methods involved the complex sample preparation, complicated procedures and costly for installation and maintenance. In order to avoid these difficulties, e-nose is used in order to classify the aging level of the lubricant oil with simpler sample preparation, less experimental procedures and lower cost compare to other instruments. The signal processing technique is implemented in order to process the raw data in order to make sure the data in a very good condition for features extraction phase. The important information that known as odor-profile then will be used for classification using Casebased Reasoning Intelligent Classification method. From this research, 100% classification result is obtained

kNN: Classification of agarwood types in oil and wooden using E-nose

Agarwood is one of the most expensive woods existed that has been used in many fields such as ceremony, religion, medical and more. There are many spe-cies of agarwood which give different quality. The most high-demand species were A. Malaccensis and A. Crassna. However, there is hard to differentiate be-tween both species either in oil or wooden medium. There is still no analytic standard method available to differentiate them. This study introduces a method of determining the types of agarwood specifically an A. Malaccensis and A. Crassna in oil and in the wooden medium using e-nose with k-Nearest Neighbour (kNN) analysis. In other to achieve that purpose, the objectives of this study were to develop the odor profile of A. Malaccensis and A. Crassna in oil and wooden medium, to classify A. Malaccensis and A. Crassna in oil and wooden medium using kNN classifier, and to measure the performance of kNN classifier on A. Malaccensis and A. Crassna on oil and wooden medium. As the result, the introduced method was able to classify both types of agarwood in both mediums with a high classification rate which is 94.5 percent accurate

The investigation of meat classification based on significant authentication features using odor-profile intelligent signal processing approach

Meat is the flesh or another edible part of an animal and includes uncooked meat prepared or otherwise but does not include meat products. Meat is the most valuable livestock product and for many people serves as their first-choice source of animal protein. Fraud meat products are causing annoyance to consumer’s, especially Muslim users. There are many cases that have been brought to the public attention regarding fraud on meat products such as incidences of meat that is labeled, certified or sold as halal may not be so. This project sets out to identify two types of different meat which is beef meat and pork meat. Therefore, the significant authentication features using odor-profile intelligent signal processing approach which is Electronic Nose (E-nose) was used to measure odor-profile from meat. E-nose is one of the chemical-based sensor arrays instruments which have a capability to measure odor-profile based sample data. The data measurement of odor-profile for different meat samples was collected based on the designated experimental procedure. Then, the normalized and their unique features were extracted using statistical tools for feature extraction. The input of features will be inserting into Case-Based Reasoning (CBR) library and intelligently classified using CBR method and will be validated based specific performance measure. From the CBR performance measures result, it is observed that the classification of CBR is 100%

A study of drag force on different type of airfoil in a subsonic wind tunnel

There had been a number of researches that investigated on drag force of airfoil in subsonic wind tunnel. This study was then conducted in order to identify drag force in subsonic wind tunnel using Computational Fluid Dynamics (CFD) software. Specifically, this research aimed to identify the drag force on ten different types NACA airfoil. NACA 0006, NACA 0009, NACA 0015, NACA 1408, NACA 1410, NACA 2408, NACA 2418, NACA 4424, NACA 6409 and NACA 6412 airfoil geometry profiles and its coordinates were generated from a NACA 4 Digits Series Generator and then designed by using ANSYS (Fluent) geometry and computed using ANSYS 14.0 software , these Computational Fluid Dynamic was used to simulate the external flow analysis on the airfoils and then the prediction of the drag force validate its simulation result with experimental result of drag force in subsonic wind tunnel. Nonetheless, the boundary condition was operated at a nominal velocity 17 m/s during the coefficient measurements, a Reynolds's number of about 1,163,798. The airfoil, with a one meter chord, was analyzed at zero degree angles of attack. Moreover, the result show NACA 4424 that have highest drag force and NACA 0006 have the lowest drag force at same boundary condition. Furthermore, the drag was generated was too small to make any significant change to the airfoil performance

Classification of Ammonia in water for Oil and Gas Industry using Case Based Reasoning (CBR)

Toxic gasses are exists in environment such as benzene, ammonia and others. Ammonia highly dissolves in water which is sources of human and other species. If the ammonia have high concentration, the effect of human health will be dangerous. Then, using proper monitoring and wastewater management the hazard can be prevented. This paper proposed the intelligence classification technique using an Electronic Nose (E-nose) measurement.The sensor array in the E - nose are used for the inputs of the Case Based Reasoning (CBR) for intelligent classification. The experimental result shows that the technique accomplished to classify with high accuracy which is 100% of accuracy

Classification of Ammonia Odor-profile Using k-NN Technique

This paper presents the application of k-NN in classifying the low and high concentration of ammonia. High concentration of ammonia in water causes serious problematic to water environment and living things in water. Instruments that can directly detect ammonia concentration without any chemical treatment added is limited. Thus, this paper presents detection of ammonia using E-nose and the classification of ammonia in water using k- nearest neighbor (k-NN)