Search results for: weed infestation forecast

Authors: R. Ramesh, K. K. Shivaraman

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The main aim of this paper is to handle the energy requirement in an efficient manner by merging the advanced digital communication and control technologies for smart grid applications. In order to reduce user home load during peak load hours, utility applies several incentives such as real-time pricing, time of use, demand response for residential customer through smart meter. However, this method provides inconvenience in the sense that user needs to respond manually to prices that vary in real time. To overcome these inconvenience, this paper proposes a convolutional neural network (CNN) with k-means clustering machine learning model which have ability to forecast energy requirement in short term, i.e., hour of the day or day of the week. By integrating our proposed technique with home energy management based on Bluetooth low energy provides predicted value to user for scheduling appliance in advanced. This paper describes detail about CNN configuration and k-means clustering algorithm for short-term energy forecasting.

Keywords: convolutional neural network, fuzzy logic, k-means clustering approach, smart home energy management

Procedia PDF Downloads 281

Authors: Faizan Hassan, Seema Kumari, V. P. Singh, Pradeep Das, Diwakar Singh Dinesh

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Phlebotomus argentipes is an established vector for Visceral Leishmaniasis in Indian subcontinent. Laboratory colonization of Sand flies is imperative in research on vectors, which requires a proper diet for their larvae and adult growth that ultimately affects their survival and fecundity. In most of the laboratories, adult Sand flies are reared on rabbit blood feeding/artificial blood feeding and their larvae on fine grinded rabbit faeces as a sole source of food. Rabbit faeces are unhygienic, difficult to handle, high mites infestation as well as owing to bad odour which creates menacing to human users ranging from respiratory problems to eye infection and most importantly it does not full fill all the nutrients required for proper growth and development. It is generally observed that the adult emergence is very low in comparison to egg hatched, which may be due to insufficient food nutrients provided to growing larvae. To check the role of food nutrients on larvae survival and adult emergence, a high protein rich artificial diet for sand fly larvae were used in this study. The composition of artificial diet to be tested includes fine grinded (9 gm each) Rice, Pea nuts & Soyabean balls. These three food ingredients are rich source of all essential amino acids along with carbohydrate and minerals which is essential for proper metabolism and growth. In this study artificial food was found significantly more effective for larval development and adult emergence than rabbit faeces alone (P value >0.05). The weight of individual larvae was also found higher in test pots than the control. This study suggest that protein plays an important role in insect larvae development and adding carbohydrate will also enhances the fecundity of insects larvae.

Keywords: artificial food, nutrients, Phlebotomus argentipes, sand fly

Procedia PDF Downloads 271

Authors: Sina Alimohammadi, Mahnaz Banihashemi, Maryam Poursharif

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Demodex is a mandatory parasite of pilosebaceous. D. folliculorum lives as a single parasite or as a number of parasites in hair follicles, and D. brevis as a single parasite living in sebaceous glands. Transmission of Demodex from one person to another requires direct skin contact; it also has a greater density in the forehead, cheeks, nose, and nasolabial folds. Demodex can cause some clinical symptoms such as follicular pityriasis, rosacea-like demodicosis, postural folliculitis, papules, seborrheic dermatitis, blepharitis, dermatitis around the lips, and hyperpigmented spots. In this study, the prevalence of Demodex species in patients referred to the dermatology department of Sayad Shirazi Hospital Gorgan, Iran, in the years 2019-2020 was investigated. Material and Methods: The study population consisted of 242 samples taken from the people referred to the dermatology department of Sayad Shirazi Hospital during the years 2019-2020, which were sampled by adhesive tape. All of the participants completed the questionnaires. The samples were examined microscopically for the presence of Demodex. Results: Out of 242 participants, 67 (27.68%) were infected with Demodex. Most cases of infection were observed in the group of 21 to 30 years (28 people; 11.57%) and then in the group of 31 to 40 years (21 people; 8.67%). Also, in the group of people under 10 years and over 60 years, no positive cases (0%) of Demodex were observed in microscopic examinations. Out of 11 variables, there was a statistically significant difference in relation to the three variables of age (P = 0.000003), use of cleansing solutions (P = 0.002), and the presence of acne (P = 0.0013). Conclusion: According to the results of this study, it was found that the incidence of Demodex in one group of acne patients is higher than in others, which emphasizes the possible role of Demodex in the pathogenesis of acne. In this study, there was an inverse relationship between the incidence of Demodex and the use of skin cleansing solutions. Also, the prevalence of Demodex is higher in the group of 20-30 years, and its prevalence does not increase with age. Due to the possibility of drug resistance in the future, regular studies on genotyping and drug resistance are recommended.

Keywords: acne, demodex, mite, prevalence

Procedia PDF Downloads 63

Authors: Figen Kadirgan, M. A. Neset Kadirgan, Gokcen A. Ciftcioglu

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Addressing food security and climate change challenges have to be done in an integrated manner. To increase food production and to reduce emissions intensity, thus contributing to mitigate climate change, food systems have to be more efficient in the use of resources. To ensure food security and adapt to climate change they have to become more resilient. The changes required in agricultural and food systems will require the creation of supporting institutions and enterprises to provide services and inputs to smallholders, fishermen and pastoralists, and transform and commercialize their production more efficiently. Thus there is continously growing need to switch to green economy where simultaneously causes reduction in carbon emissions and pollution, enhances energy and resource-use efficiency; and prevents the loss of biodiversity and ecosystem services. Smart Agriculture takes into account the four dimensions of food security, availability, accessibility, utilization, and stability. It is well known that, the increase in world population will strengthen the population-food imbalance. The emphasis on reduction of food losses makes a point on production, on farmers, on increasing productivity and income ensuring food security. Where also small farmers enhance their income and stabilize their budget. The use of solar drying for agricultural, marine or meat products is very important for preservation. Traditional sun drying is a relatively slow process where poor food quality is seen due to an infestation of insects, enzymatic reactions, microorganism growth and micotoxin development. In contrast, solar drying has a sound solution to all these negative effects of natural drying and artificial mechanical drying. The technical directions in the development of solar drying systems for agricultural products are compact collector design with high efficiency and low cost. In this study, using solar selective surface produced in Selektif Teknoloji Co. Inc. Ltd., solar dryers with high efficiency will be developed and a feasibility study will be realized.

Keywords: energy, renewable energy, solar collector, solar drying

Procedia PDF Downloads 203

Authors: Marjan Golmaryami, Marzieh Behzadi

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Recent developments in stock exchange highlight the need for an efficient and accurate method that helps stockholders make better decision. Since stock markets have lots of fluctuations during the time and different effective parameters, it is difficult to make good decisions. The purpose of this study is to employ artificial neural network (ANN) which can deal with time series data and nonlinear relation among variables to forecast next day stock price. Unlike other evolutionary algorithms which were utilized in stock exchange prediction, we trained our proposed neural network with metaheuristic bat algorithm, with fast and powerful convergence and applied it in stock price prediction for the first time. In order to prove the performance of the proposed method, this research selected a 7 year dataset from Parsian Bank stocks and after imposing data preprocessing, used 3 types of ANN (back propagation-ANN, particle swarm optimization-ANN and bat-ANN) to predict the closed price of stocks. Afterwards, this study engaged MATLAB to simulate 3 types of ANN, with the scoring target of mean absolute percentage error (MAPE). The results may be adapted to other companies stocks too.

Keywords: artificial neural network (ANN), bat algorithm, particle swarm optimization algorithm (PSO), stock exchange

Procedia PDF Downloads 523

Authors: J. Julius Adebayo

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The objective of this paper is to assess the agro-climatic condition of Ibadan in the rain forest ecological zone of Nigeria, using rainfall pattern and temperature between 1978-2018. Data on rainfall and temperature in Ibadan, Oyo State for a period of 40 years were obtained from Meteorological Section of Forestry Research Institute of Nigeria, Ibadan and Oyo State Meteorology Centre. Time series analysis was employed to analyze the data. The trend revealed that rainfall is decreasing slowly and temperature is averagely increasing year after year. The model for rainfall and temperature are Yₜ = 1454.11-8*t and Yₜ = 31.5995 + 2.54 E-02*t respectively, where t is the time. On this basis, a forecast of 20 years (2019-2038) was generated, and the results showed a further downward trend on rainfall and upward trend in temperature, this indicates persistence rainfall shortage and very hot weather for agricultural practices in the southwest rain forest ecological zone. Suggestions on possible solutions to avert climate change crisis and also promote climate-smart agriculture for sustainable food and nutrition security were also discussed.

Keywords: climate change, rainfall pattern, temperature, time series analysis, food and nutrition security

Procedia PDF Downloads 111

Authors: A. Porshnev, A. Zaporozhtchuk

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Motivation regarded as a key factor of labor turnover, is especially important for volunteers working on an altruistic basis in NGO. Despite the motivational letter, candidate selection depends on the impression of the selection committee, which can be subject to human bias. We expect that structured and unstructured information provided in motivation letters could be used to improve candidate selection procedures. In our paper, we perform qualitative and quantitative analysis of 2280 motivation letters, create logistic regression, and build a decision tree to improve selection procedures. Our analysis showed that motivation factors are significant and enable human resources department to forecast labor turnover and provide extra information to demographic, professional and timing questions. In spite of the average level of accuracy the model demonstrates the selection procedures of company of under consideration can be improved. We also discuss interrelation between answers to open and closed motivation questions, recommend changes in motivational letter templates to ensure more relevant information about applicants and further steps to create more accurate model.

Keywords: decision trees, logistic regression, model, motivational letter, non-governmental organization, retention, turnover

Procedia PDF Downloads 151

Authors: Salahaeddine Sbai

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Air quality analysis using machine learning models is a method employed to assess and predict air pollution levels. This approach leverages the capabilities of machine learning algorithms to analyze vast amounts of air quality data and extract valuable insights. By training these models on historical air quality data, they can learn patterns and relationships between various factors such as weather conditions, pollutant emissions, and geographical features. The trained models can then be used to predict air quality levels in real-time or forecast future pollution levels. This application of machine learning in air quality analysis enables policymakers, environmental agencies, and the general public to make informed decisions regarding health, environmental impact, and mitigation strategies. By understanding the factors influencing air quality, interventions can be implemented to reduce pollution levels, mitigate health risks, and enhance overall air quality management. Climate change is having significant impacts on Morocco, affecting various aspects of the country's environment, economy, and society. In this study, we use some machine learning models under python environment to predict and analysis air quality change over North of Morocco to evaluate the climate change impact on agriculture.

Keywords: air quality, machine learning models, pollution, pollutant emissions

Procedia PDF Downloads 58

Authors: M. R. Gholizadeh, N. Bhuiyan, M. Salari

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In the near future, companies will be increasingly forced to shift their activities along a new road in order to decrease the harmful effects of their design, production and after-life on our environment. Products must meet environmental standards to not only prevent penalties but to consider the sustainability for future generations. However, the most important factor that companies will face is selecting a reasonable strategy to maximize their profit. Thus, companies need to have precise forecast from their profit after design stage through Trade-off analysis. This paper is an attempt to introduce a mathematical model that considers effective factors that impact the total profit when products are designed for resource and energy efficiency or recyclability. The modification is according to different strategies based on a Cost-Volume-Profit model. Here, the cost structure consists of Recycling cost, Development cost, Ramp-up cost, Production cost, and Pollution cost. Also, the model shows the effect of implementation of design for recyclable on revenue structure through revenue of used parts and revenue of recycled materials. A numerical example is used to evaluate the proposed model. Results show that fulfillment of Green Product Development not only can reduce the environmental impact of products but also it will increase profit of company in long term.

Keywords: green product, design for environment, C-V-P model, trade-off analysis

Procedia PDF Downloads 288

Authors: Amir Hamed Abd-Elrahman, Mohamed Bessat

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450 fattening cattle and buffaloes aged from 6 to 30 months old were examined clinically to determine patterns of occurrence of hemoparasitic diseases and the efficacy of different anti theilerial drugs. 420 animals examined clinically to determine relation between different outbreak of FMD and LSD in Egypt 2012- 2013 and haemoprotozoal diseases. The clinical pictures of haemoprotozoal diseases are variable, from sever to mild, depending on the endemic situation which governed by frequent previous exposure and tick infestation. B. bigemina is the most common haemoprotozoal diseases in the area of study and the infection rate in a descending manner for B. bigemina, A. marginale and T. annulata were 20%, 9.7% and 6.6% respectively. The species susceptibility of B. bigemina and T. annulata showed a higher incidence in cattle than buffaloes while in A. marginale showed a little difference in cattle and buffaloes susceptibility by 10% and 9.2% respectively. The breed susceptibility of B. bigemina and T. annulata showed a higher incidence in crossbred cattle than native baladi cattle while A. marginale showed a higher incidence in native baladi cattle than crossbred cattle. The maximal infection rates were recorded during summer months. The infection rates of B. bigemina and A. marginale were higher among young animals over 6 months and declined above 2 year old while in T. annulata the infection rates were lower among young animals and increased above 2 year old. The case fatality of T. annulata was higher than A. marginale and B. bigemina. Efficacy of different anti theilerial drugs were studied, cure rate of chlouroquine group and Butalex group were 60% disappearance of schizont in lymph node smear after 9 days and 5 days respectively while cure rate of Oxytetracycline Dihydrate (Alamycine) group 20% with disappearance of schizont in lymph node smear after 14 days. FMD and LSD infection enhancement the occurrence of bovine haemoprotozoal diseases.

Keywords: Babesia bigemina, Anaplasma marginale, Theileria annulata, FMD, LSD, ephemeral fever

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Authors: Amir Andria Gad Shehata

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Minimizing the inventory cost, optimizing the inventory quantities, and increasing system operational availability are the main motivations to enhance forecasting demand of spare parts in a major power utility company in Medina. This paper reports in an effort made to optimize the orders quantities of spare parts by improving the method of forecasting the demand. The study focuses on equipment that has frequent spare parts purchase orders with uncertain demand. The pattern of the demand considers a lumpy pattern which makes conventional forecasting methods less effective. A comparison was made by benchmarking various methods of forecasting based on experts’ criteria to select the most suitable method for the case study. Three actual data sets were used to make the forecast in this case study. Two neural networks (NN) approaches were utilized and compared, namely long short-term memory (LSTM) and multilayer perceptron (MLP). The results as expected, showed that the NN models gave better results than traditional forecasting method (judgmental method). In addition, the LSTM model had a higher predictive accuracy than the MLP model.

Keywords: spare part, spare part inventory, inventory model, optimization, maintenanceneural network, LSTM, MLP, forecasting demand, inventory management

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Authors: Oluseun S. Oyelakin, Olalekan W. Olaniyi

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A field experiment was conducted at Ido town in Ido Local Government Area of Oyo state, Nigeria to determine the effects of intercropping maize (Zea mays L.) with Jack bean (Canavalia ensiformis L.) at different spacing and weeding regimes on crops productivity. The treatments were 2 x 2 x 3 factorial arrangement involving two spatial crop arrangements. Spacing of 75 cm x 50 cm and 90 cm x 42 cm (41.667 cm) with two plants per stand resulted in plant population of approximately 53,000 plants/hectare. Also, Randomized Complete Block Design (RCBD) with two cropping patterns (sole and intercrop), three weeding regimes (weedy check, weeds once, and weed twice) with three replicates was used. Data were analyzed with SAS (Statistical Analysis System) and statistical means separated using Least Significant Difference (LSD) (P ≤ 0.05). Intercropping and crop spacing did not have significant influence on the growth parameters and yield parameters. The maize grain yield of 1.11 t/ha obtained under sole maize was comparable to 1.05 t/ha from maize/jack beans. Weeding regime significantly influenced growth and yields of maize in intercropping with Jack beans. Weeding twice resulted in significantly higher growth than that of the other weeding regimes. Plant height at 6 Weeks After Sowing (WAS) under weeding twice regime (3 and 6 WAS) was 83.9 cm which was significantly different from 67.75 cm and 53.47 cm for weeding once (3 WAS) and no weeding regimes respectively. Moreover, maize grain yield of 1.3 t/ha obtained from plots weeded twice was comparable to that of 1.23 t/ha from single weeding and both were significantly higher than 0.71 t/ha maize grain yield obtained from the no weeding control. The dry matter production of Jack beans reduced at some growth stages due to intercropping of maize with Jack beans though with no significance effect on the other growth parameters of the crop. There was no effect on the growth parameters of Jack beans in maize/jack beans intercrop based on cropping spacing while comparable growth and dry matter production in Jack beans were produced in maize/Jack beans mixture with single weeding.

Keywords: crop spacing, intercropping, growth parameter, weeding regime, sole cropping, WAS, week after sowing

Procedia PDF Downloads 117

Authors: Sara Patricia Ibarra-Zavaleta, Rabindranarth Romero-Lopez, Rosario Langrave, Annie Poulin, Gerald Corzo, Mathias Glaus, Ricardo Vega-Azamar, Norma Angelica Oropeza

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The progressive change in climatic conditions worldwide has increased frequency and severity of extreme hydrometeorological events (EHE). Mexico is an example; this has been affected by the presence of EHE leaving economic, social and environmental losses. The objective of this research was to apply a Canadian distributed hydrological model (DHM) to tropical conditions and to evaluate its capacity to predict flows in a basin in the central Gulf of Mexico. In addition, the DHM (once calibrated and validated) was used to calculate the theoretical hydraulic power and the performance to predict streamflow before the presence of an EHE. The results of the DHM show that the goodness of fit indicators between the observed and simulated flows in the calibration process (NSE=0.83, RSR=0.021 and BIAS=-4.3) and validation: temporal was assessed at two points: point one (NSE=0.78, RSR=0.113 and BIAS=0.054) and point two (NSE=0.825, RSR=0.103 and BIAS=0.063) are satisfactory. The DHM showed its applicability in tropical environments and its ability to characterize the rainfall-runoff relationship in the study area. This work can serve as a tool for identifying vulnerabilities before floods and for the rational and sustainable management of water resources.

Keywords: HYDROTEL, hydraulic power, extreme hydrometeorological events, streamflow

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Authors: Anupam Purwar

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In conventional consumer product industry, stockouts are taken care by carrying buffer stock to check underserving caused by changes in customer demand, incorrect forecast or variability in lead times. But, for food outlets, the alternate of carrying buffer stock is unviable because of indispensable need to serve freshly cooked meals. Besides, the food outlet being the sole provider has no incentives to reduce stockouts, as they have no fear of losing revenue, gross profit, customers and market share. Hence, innovative, easy to implement and practical ways of addressing the twin problem of long queues and poor customer experience needs to be investigated. Current work analyses the demand pattern of 11 different food items across a routine day. Based on this optimum resource allocation for all food items has been carried out by solving a linear programming problem with cost minimization as the objective. Concurrently, recommendations have been devised to address this demand and supply side problem keeping in mind their practicability. Currently, the recommendations are being discussed and implemented at ISB (Indian School of Business) Hyderabad campus.

Keywords: F&B industry, resource allocation, demand management, linear programming, LP, queuing analysis

Procedia PDF Downloads 112

Authors: Ishani D. Goonasekara, Erio Camporesi, Timur Bulgakov, Rungtiwa Phookamsak, Kevin D. Hyde

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Phaeosphaeriaceae comprises a large number of species occurring mainly on grasses and cereal crops as endophytes, saprobes and especially pathogens. Parastagonospora is an important genus in Phaeosphaeriaceae that includes pathogens causing leaf and glume blotch on cereal crops. Currently, there are fifteen Parastagonospora species described, including both pathogens and saprobes. In this study, one sexual morph species and an asexual morph species, occurring as saprobes on members of Poaceae are introduced based on morphology and a combined molecular analysis of the LSU, SSU, ITS, and RPB2 gene sequence data. The sexual morph species Parastagonospora elymi was isolated from a Russian sample of Elymus repens, a grass commonly known as couch grass, and important for grazing animals, as a weed and used in traditional Austrian medicine. P. elymi is similar to the sexual morph of P. avenae in having cylindrical asci, bearing 8, overlapping biseriate, fusiform ascospores but can be distinguished by its subglobose to conical shaped, wider ascomata. In addition, no sheath was observed surrounding the ascospores. The asexual morph species was isolated from a specimen from Italy, on Dactylis glomerata, a commonly found grass distributed in temperate regions. It is introduced as Parastagonospora macrouniseptata, a coelomycete, and bears a close resemblance to P. allouniseptata and P. uniseptata in having globose to subglobose, pycnidial conidiomata and hyaline, cylindrical, 1-septate conidia. However, the new species could be distinguished in having much larger conidiomata. In the phylogenetic analysis which consisted of a maximum likelihood and Bayesian analysis P. elymi showed low bootstrap support, but well segregated from other strains within the Parastagonospora clade. P. neoallouniseptata formed a sister clade with P. allouniseptata with high statistical support.

Keywords: dothideomycetes, multi-gene analysis, Poaceae, saprobes, taxonomy

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Authors: Kashif Hassan, M.A. Ahanger

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Floods have become more frequent and severe due to effects of global climate change and human alterations of the natural environment. Flood prediction/ forecasting and control is one of the greatest challenges facing the world today. The forecast of floods is achieved by the use of hydraulic models such as HEC-RAS, which are designed to simulate flow processes of the surface water. Extreme flood events in river Jhelum , lasting from a day to few are a major disaster in the State of Jammu and Kashmir, India. In the present study HEC-RAS model was applied to two different reaches of river Jhelum in order to estimate the flood levels corresponding to 25, 50 and 100 year return period flood events at important locations and to deduce flood vulnerability of important areas and structures. The flow rates for the two reaches were derived from flood-frequency analysis of 50 years of historic peak flow data. Manning's roughness coefficient n was selected using detailed analysis. Rating Curves were also generated to serve as base for determining the boundary conditions. Calibration and Validation procedures were applied in order to ensure the reliability of the model. Sensitivity analysis was also performed in order to ensure the accuracy of Manning's n in generating water surface profiles.

Keywords: flood plain, HEC-RAS, Jhelum, return period

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Authors: Karim Hamidi Machekposhti, Hossein Sedghi, Abdolrasoul Telvari, Hossein Babazadeh

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Floods have huge environmental and economic impact. Therefore, flood prediction is given a lot of attention due to its importance. This study analysed the annual maximum streamflow (discharge) (AMS or AMD) of Karkheh River in Karkheh River Basin for flood predicting using ARIMA model. For this purpose, we use the Box-Jenkins approach, which contains four-stage method model identification, parameter estimation, diagnostic checking and forecasting (predicting). The main tool used in ARIMA modelling was the SAS and SPSS software. Model identification was done by visual inspection on the ACF and PACF. SAS software computed the model parameters using the ML, CLS and ULS methods. The diagnostic checking tests, AIC criterion, RACF graph and RPACF graphs, were used for selected model verification. In this study, the best ARIMA models for Annual Maximum Discharge (AMD) time series was (4,1,1) with their AIC value of 88.87. The RACF and RPACF showed residuals’ independence. To forecast AMD for 10 future years, this model showed the ability of the model to predict floods of the river under study in the Karkheh River Basin. Model accuracy was checked by comparing the predicted and observation series by using coefficient of determination (R²).

Keywords: time series modelling, stochastic processes, ARIMA model, Karkheh river

Procedia PDF Downloads 267

Authors: Givi Gavardashvili, Eduard Kukhalashvili, Tamriko Supatashvili, Giorgi Natroshvili, Konstantine Bziava, Irma Qufarashvili

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In the article to research water levels in the Zhinvali water reservoirs by field and theoretical research and using GPS and GIS technologies has been established dynamic of water reservoirs changes in the suitable coordinates and has been made water reservoir maps and is lined in the 3D format. By using of GPS coordinates and digital maps has been established water horizons of Zhinvali water reservoir in the absolute marks and has been calculated water levels volume. To forecast the filling of the Zhinvali water reservoir by solid sediment in 2018 conducted field experimental researches in the catchment basin of river Tetri (White) Aragvi. It has been established main hydrological and hydraulic parameters of the active erosion-debris flow tributaries of river Tetri Aragvi. It has been calculated erosion coefficient considering the degradation of the slope. By calculation is determined, that in the river Tetri Aragvi catchment basin the value of 1% maximum discharge changes Q1% = 70,0 – 550,0 m3/sec, and erosion coefficient - E = 0,73 - 1,62, with suitable fifth class of erosion and intensity 50-100 tone/hectare in the year.

Keywords: Zhinvali soil dam, water reservoirs, water levels, erosion, debris flow

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Authors: Nasrin Bakhshizadeh, Ashkan Forootan

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A major problem that affects the quality control of polymer in the industrial polymerization is the lack of suitable on-line measurement tools to evaluate the properties of the polymer such as melt and density indices. Controlling the polymerization in ordinary method is performed manually by taking samples, measuring the quality of polymer in the lab and registry of results. This method is highly time consuming and leads to producing large number of incompatible products. An online application for estimating melt index and density proposed in this study is a neural network based on the input-output data of the polyethylene production plant. Temperature, the level of reactors' bed, the intensity of ethylene mass flow, hydrogen and butene-1, the molar concentration of ethylene, hydrogen and butene-1 are used for the process to establish the neural model. The neural network is taught based on the actual operational data and back-propagation and Levenberg-Marquart techniques. The simulated results indicate that the neural network process model established with three layers (one hidden layer) for forecasting the density and the four layers for the melt index is able to successfully predict those quality properties.

Keywords: polyethylene, polymerization, density, melt index, neural network

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Authors: Sai Sudarshan, Harsh Vyas, Riddhiman Sherlekar

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The oil and gas industry has been unwilling to adopt stochastic definition of reserves. Nevertheless, Monte Carlo simulation methods have gained acceptance by engineers, geoscientists and other professionals who want to evaluate prospects or otherwise analyze problems that involve uncertainty. One of the common applications of Monte Carlo simulation is the estimation of recoverable hydrocarbon from a reservoir.Monte Carlo Simulation makes use of random samples of parameters or inputs to explore the behavior of a complex system or process. It finds application whenever one needs to make an estimate, forecast or decision where there is significant uncertainty. First, the project focuses on performing Monte-Carlo Simulation on a given data set using U. S Department of Energy’s MonteCarlo Software, which is a freeware e&p tool. Further, an algorithm for simulation has been developed for MATLAB and program performs simulation by prompting user for input distributions and parameters associated with each distribution (i.e. mean, st.dev, min., max., most likely, etc.). It also prompts user for desired probability for which reserves are to be calculated. The algorithm so developed and tested in MATLAB further finds implementation in Python where existing libraries on statistics and graph plotting have been imported to generate better outcome. With PyQt designer, codes for a simple graphical user interface have also been written. The graph so plotted is then validated with already available results from U.S DOE MonteCarlo Software.

Keywords: simulation, probability, confidence interval, sensitivity analysis

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Authors: Chisomo Patrick Kumbuyo, Katsuyuki Shimizu, Hiroshi Yasuda, Yoshinobu Kitamura

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This study deals with a way of predicting Malawi rainfall from global sea surface temperature (SST) using a simple multiple regression model. Monthly rainfall data from nine stations in Malawi grouped into two zones on the basis of inter-station rainfall correlations were used in the study. Zone 1 consisted of Karonga and Nkhatabay stations, located in northern Malawi; and Zone 2 consisted of Bolero, located in northern Malawi; Kasungu, Dedza, Salima, located in central Malawi; Mangochi, Makoka and Ngabu stations located in southern Malawi. Links between Malawi rainfall and SST based on statistical correlations were evaluated and significant results selected as predictors for the regression models. The predictors for Zone 1 model were identified from the Atlantic, Indian and Pacific oceans while those for Zone 2 were identified from the Pacific Ocean. The correlation between the fit of predicted and observed rainfall values of the models were satisfactory with r=0.81 and 0.54 for Zone 1 and 2 respectively (significant at less than 99.99%). The results of the models are in agreement with other findings that suggest that SST anomalies in the Atlantic, Indian and Pacific oceans have an influence on the rainfall patterns of Southern Africa.

Keywords: Malawi rainfall, forecast model, predictors, SST

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Authors: E. U. Dampage, Ovindi D. Bandara, Vinushi S. Waraketiya, Samitha S. R. De Silva, Yasiru S. Gunarathne

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The knowledge of water inflow figures is paramount in decision making on the allocation for consumption for numerous purposes; irrigation, hydropower, domestic and industrial usage, and flood control. The understanding of how reservoir inflows are affected by different climatic and hydrological conditions is crucial to enable effective water management and downstream flood control. In this research, we propose a method using a Long Short Term Memory (LSTM) Artificial Neural Network (ANN) to assist the aforesaid decision-making process. The Kotmale reservoir, which is the uppermost reservoir in the Mahaweli reservoir complex in Sri Lanka, was used as the test bed for this research. The ANN uses the runoff in the Kotmale reservoir catchment area and the effect of Sea Surface Temperatures (SST) to make a forecast for seven days ahead. Three types of ANN are tested; Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and LSTM. The extensive field trials and validation endeavors found that the LSTM ANN provides superior performance in the aspects of accuracy and latency.

Keywords: convolutional neural network, CNN, inflow, long short-term memory, LSTM, multi-layer perceptron, MLP, neural network

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Authors: R. Tlili Yaakoubi, H.Nakouri, O. Blanpain, S. Lallahem

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The current tools for real time management of sewer systems are based on two software tools: the software of weather forecast and the software of hydraulic simulation. The use of the first ones is an important cause of imprecision and uncertainty, the use of the second requires temporal important steps of decision because of their need in times of calculation. This way of proceeding fact that the obtained results are generally different from those waited. The major idea of this project is to change the basic paradigm by approaching the problem by the "automatic" face rather than by that "hydrology". The objective is to make possible the realization of a large number of simulations at very short times (a few seconds) allowing to take place weather forecasts by using directly the real time meditative pluviometric data. The aim is to reach a system where the decision-making is realized from reliable data and where the correction of the error is permanent. A first model of control laws was realized and tested with different return-period rainfalls. The gains obtained in rejecting volume vary from 19 to 100 %. The development of a new algorithm was then used to optimize calculation time and thus to overcome the subsequent combinatorial problem in our first approach. Finally, this new algorithm was tested with 16- year-rainfall series. The obtained gains are 40 % of total volume rejected to the natural environment and of 65 % in the number of discharges.

Keywords: automation, optimization, paradigm, RTC

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Authors: Nina Miklavčič, Urška Kugovnik, Natalia Galkina, Primož Ribarič, Rudi Vončina

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Today, the need for weather predictions is deeply rooted in the everyday life of people as well as it is in industry. The forecasts influence final decision-making processes in multiple areas, from agriculture and prevention of natural disasters to air traffic regulations and solutions on a national level for health, security, and economic problems. Namely, in Slovenia, alongside other existing forms of application, weather forecasts are adopted for the prognosis of electrical current transmission through powerlines. Meteorological parameters are one of the key factors which need to be considered in estimations of the reliable supply of electrical energy to consumers. And like for any other measured value, the knowledge about measurement uncertainty is also critical for the secure and reliable supply of energy. The estimation of measurement uncertainty grants us a more accurate interpretation of data, a better quality of the end results, and even a possibility of improvement of weather forecast models. In the article, we focused on the estimation of measurement uncertainty of the diagnostic microscale meteorological model CALMET. For the purposes of our research, we used a network of meteorological stations spread in the area of our interest, which enables a side-by-side comparison of measured meteorological values with the values calculated with the help of CALMET and the measurement uncertainty estimation as a final result.

Keywords: uncertancy, meteorological model, meteorological measurment, CALMET

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Authors: Mujahid Manzoor, Muhammad Shafique, Jam Nazeer Ahmad, Farman Ahmad, Muhammad Ali, Muhammad Rizwan Tariq, Shahbaz Ahmad, Muhammad Saleem Haider

Abstract:

Date palms (P. dactylifera L.) are prominent in the Kingdom of Saudi Arabia (KSA), Iran, UAE, and Iraq, as well as Algeria, Egypt, and Pakistan. Insect pests' attacks on different palm cultivars adversely affected their production in the last few decades. Pakistan ranked sixth for date production around the world. The most commercially grown cultivars are Aseel, Dhaki, Falsi, Karbalai, Mozawati, Jan Swore, Kohra, Hillawi, Kohra, and Begum Jhangi. Red palm weevils are considered as hazardous insect pests responsible for economic loss in palm orchards. This research work depicts the infestation of red palm weevils in eleven different palm cultivars (Hillawi, Mozawati, Kechanr, Aseel, Shamrani, Khudravi, Dhaki, Zeri, Kobra, Zaidi, Denda), which frequently grow in different regions of Pakistan through Y-shaped olfactometer analysis. In addition, the level of both antixenosis and antibiosis was spotted by examining the various parameters such as %age lure of weevils of mature females, general count of laid eggs in addition to their activeness. Furthermore, obtained results depicted that a positive contact was established with minimum antixenotic consequence revealed by a cultivar, “Hillawi” among most hold of RPW adults (22.32%), number of eggs laid (16.66%) and egg hatching (84.66%), while other cultivars, including Mozawati, Aseel, Kechanr, Shamrani, Khudravi, Dhaki, Zeri, and Zaidi, exhibited a greater level of antixenosis. Moreover, “Hillawi” documented the maximum number of eggs, while Kechanr, Mozawati, Aseel Kobra, and Denda showed minimum attraction by red palm weevils. Maximum red palm weevils were attracted in an olfactometer assay of sugarcane varieties.

Keywords: P. dactylifera, R. ferrugineus, olfactometer, antixenosis

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Authors: N. Sabiyath Fatima, R. K. Shanmugasundaram

Abstract:

In Mobile Ad-hoc Networks (MANETS), every node performs both as transmitter and receiver. The existing backoff models do not exactly forecast the performance of the wireless network. Also, the existing models experience elevated packet collisions. Every time a collision happens, the station’s contention window (CW) is doubled till it arrives at the utmost value. The main objective of this paper is to diminish collision by means of contention window Multiplicative Increase Decrease Backoff (CWMIDB) scheme. The intention of rising CW is to shrink the collision possibility by distributing the traffic into an outsized point in time. Within wireless Ad hoc networks, the CWMIDB algorithm dynamically controls the contention window of the nodes experiencing collisions. During packet communication, the backoff counter is evenly selected from the given choice of [0, CW-1]. At this point, CW is recognized as contention window and its significance lies on the amount of unsuccessful transmission that had happened for the packet. On the initial transmission endeavour, CW is put to least amount value (C min), if transmission effort fails, subsequently the value gets doubled, and once more the value is set to least amount on victorious broadcast. CWMIDB is simulated inside NS2 environment and its performance is compared with Binary Exponential Backoff Algorithm. The simulation results show improvement in transmission probability compared to that of the existing backoff algorithm.

Keywords: backoff, contention window, CWMIDB, MANET

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Authors: Deepa Kundu, Prabhakar Sharma, Sayan Bhattacharya, Jianying Shang

Abstract:

The discharge of dye-containing effluents in the water bodies has raised concern due to the potential hazards related to their toxicity in the environment. There are various treatment technologies available for the removal of dyes from wastewaters. The use of biosorbent to remove dyes from wastewater is one of the effective and inexpensive techniques. In the study, the adsorption of phenothiazine dye methylene blue onto biosorbent prepared from Lantana camara L. has been studied in aqueous solutions. The batch adsorption experiments were conducted and the effects of various parameters such as pH (3-12), contact time, adsorbent dose (100-400 mg/L), initial dye concentration (5-20 mg/L), and temperature (303, 313 and 323 K) were investigated. The prepared leaf (BCL600) and shoot (BCS600) biochar of Lantana were characterized using FTIR, SEM, elemental analysis, and zeta potential (pH~7). A comparison between the adsorption potential of both the biosorbent was also evaluated. The results indicated that the amount of methylene blue dye (mg/g) adsorbed onto the surface of biochar was highly dependent on the pH of the dye solutions as it increased with an increase in pH from 3 to 12. It was observed that the dye treated with BCS600 and BCL600 attained an equilibrium within 60 and 100 minutes, respectively. The rate of the adsorption process was determined by performing the Lagergren pseudo-first-order and pseudo-second-order kinetics. It was found that dye treated with both BCS600 and BCL600 followed pseudo-second-order kinetics implying the multi-step nature of the adsorption process involving external adsorption and diffusion of dye molecules into the interior of the adsorbents. The data obtained from batch experiments were fitted well with Langmuir and Freundlich isotherms (R² > 0.98) to indicate the multilayer adsorption of dye over the biochar surfaces. The thermodynamic studies revealed that the adsorption process is favourable, spontaneous, and endothermic in nature. Based on the results, the inexpensive and easily available Lantana camara biomass can be used to remove methylene blue dye from wastewater. It can also help in managing the growth of the notorious weed in the environment.

Keywords: adsorption kinetics, biochar, Lantana camara, methylene blue dye, possible mechanism, thermodynamics

Procedia PDF Downloads 107

Authors: Lim Sanny, Felix Christian

Abstract:

The use of heavy equipment in Indonesia is always increasing. Cost reduction in procurement of spare parts is the aim of the company. The spare parts in this research are focused in the kind of filters. On the early step, the choosing of priority filter will be studied further by using the ABC analysis. To find out future demand of the filter, this research is using demand forecast by utilizing the QM software for windows. And to find out the best method of inventory control for each kind of filter is by comparing the total cost of Economic Order Quantity and Quick response inventory method. For the three kind of filters which are Cartridge, Engine oil – pn : 600-211-123, Element, Transmission – pn : 424-16-11140, and Element, Hydraulic – pn : 07063-01054, the best forecasting method is Linear regression. The best method for inventory control of Cartridge, Engine oil – pn : 600-211-123 and Element, Transmission – pn : 424-16-11140, is Quick Response Inventory, while the best method for Element, Hydraulic – pn : 07063-01054 is Economic Order Quantity.

Keywords: strategy, inventory, ABC analysis, forecasting, economic order quantity, quick response inventory

Procedia PDF Downloads 342

Authors: Keshav Raj Acharya, Prabina Sharma

Abstract:

Sugandhakokila (Cinnamomum glaucescens Nees. Dury) is a large evergreen native tree species; mostly confined naturally in mid-hills of Rapti Zone of Nepal. The species is identified as prioritized for agro-technology development as well as for research and development by a department of plant resources. This species is band for export outside the country without processing by the government of Nepal to encourage the value addition within the country. The present study was carried out in Chillikot village of Dang district to find out the economic contribution of C. glaucescens in the local economy and to document the major conservation threats for this species. Participatory Rural Appraisal (PRA) tools such as Household survey, key informants interviews and focus group discussions were carried out to collect the data. The present study reveals that about 1.7 million Nepalese rupees (NPR) have been contributed annually in the local economy of 29 households from the collection of C. glaucescens berries in the study area. The average annual income of each family was around NPR 67,165.38 (US$ 569.19) from the sale of the berries which contributes about 53% of the total household income. Six different value chain actors are involved in C. glaucescens business. Maximum profit margin was taken by collector followed by producer, exporter and processor. The profit margin was found minimum to regional and village traders. The total profit margin for producers was NPR 138.86/kg, and regional traders have gained NPR 17/kg. However, there is a possibility to increase the profit of producers by NPR 8.00 more for each kg of berries through the initiation of community forest user group and village cooperatives in the area. Open access resource, infestation by an insect to over matured trees and browsing by goats were identified as major conservation threats for this species. Handing over the national forest as a community forest, linking the producers with the processor through organized market channel and replacing the old tree through new plantation has been recommended for future.

Keywords: community forest, conservation threats, C. glaucescens, value chain analysis

Procedia PDF Downloads 103

Authors: Rabaa Tlili Yaakoubi, Hind Nakouri, Olivier Blanpain

Abstract:

The current tools for real time management of sewer systems are based on two software tools: the software of weather forecast and the software of hydraulic simulation. The use of the first ones is an important cause of imprecision and uncertainty, the use of the second requires temporal important steps of decision because of their need in times of calculation. This way of proceeding fact that the obtained results are generally different from those waited. The major idea of the CARDIO project is to change the basic paradigm by approaching the problem by the "automatic" face rather than by that "hydrology". The objective is to make possible the realization of a large number of simulations at very short times (a few seconds) allowing to take place weather forecasts by using directly the real time meditative pluviometric data. The aim is to reach a system where the decision-making is realized from reliable data and where the correction of the error is permanent. A first model of control laws was realized and tested with different return-period rainfalls. The gains obtained in rejecting volume vary from 40 to 100%. The development of a new algorithm was then used to optimize calculation time and thus to overcome the subsequent combinatorial problem in our first approach. Finally, this new algorithm was tested with 16- year-rainfall series. The obtained gains are 60% of total volume rejected to the natural environment and of 80 % in the number of discharges.

Keywords: RTC, paradigm, optimization, automation

Procedia PDF Downloads 258