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This study uses cyclohexanol – a high-carbon, cyclic bio-alcohol which is a derivative of lignocellulosic biomass – in blended form with diesel to power a direct-injection single-cylinder diesel en...

n-Octanol is a promising biofuel synthesized from biomass with several properties closer to diesel than the more popularly researched n-butanol. This study investigates the effects of injection tim...

n-Octanol (C8H17OH) is an advanced biofuel derived from ligno-cellulosic biomass that is suitable for compression ignition technology with several properties closer to fossil diesel. This study analyses the performance and emissions of a direct-injection (DI) diesel engine fueled with n-octanol/diesel blends containing 10% (OCT10), 20% (OCT20) and 30%(OCT30) by volume of n-octanol using a 3 × 3 full-factorial experimental design matrix that considers blend composition of n-octanol in diesel, exhaust gas recirculation (EGR) rates of 10%, 15% and 20% and injection timings of 19°, 21° and 23° crank angle (CA) before top dead centre (bTDC) as factors. Models for oxides of nitrogen (NOx), smoke, brake thermal efficiency (BTE) and brake specific fuel consumption (BSFC) were developed using response surface methodology (RSM) and were found to be significant statistically. The variation of EGR had a considerable effect on both BTE and BSFC of the engine followed by blend composition and injection timing. Best performance (BTE = 37.06%, BSFC = 0.23kg/kWh) was delivered by OCT10 at 10% EGR and 23°CA while the lowest performance (BTE = 30.95%, BSFC = 0.28kg/kWh) was by OCT30 at 20% EGR and 19°CA. Injection timing was found to have the highest effect on NOx emissions while EGR affected smoke opacity to the maximum. NOx was found to decrease from 1790 ppm (for OCT10 at 10% EGR and 23°CA) to as low as 410 ppm (for OCT30 at 20% EGR and 19°CA). Smoke opacity was found to decrease from 94.2% (for OCT10 at 20% EGR and 19°CA) to as low as 43% (for OCT30 at 10% EGR and 23°CA). Desirability approach was used to determine the best combination of blend composition of n-octanol, EGR and injection timing for minimising smoke, NOx and BSFC simultaneously. 17% by volume of n-octanol/diesel blend injected at 20° CA bTDC and 10% EGR was predicted to be optimum which delivered a simultaneous reduction of NOx (−47.4%), smoke (−21.08%) and BSFC (−8%) during confirmatory tests with a reasonable accuracy of within 4%. This method is robust and could be employed to other small engines for developing models that can predict engine characteristics with reasonable accuracy.

In this paper, a predictive mathematical model is proposed to identify the best alternatives from the given set of alternatives characterized by multiple criteria. An objective function is developed to find the ranking index of the alternatives. A new Comprehensive-Technique for Order Preference by Similarity to Ideal Solution (C-TOPSIS) method is proposed which combines the comprehensive weights of the criteria with TOPSIS method. The proposed predictive mathematical model generates a ranking of the alternatives. An experimental study has been carried out by taking agricultural data set of rice paddy crop to demonstrate and validate the developed model. The results show significant correlation between the ranks obtained by the proposed model and the ranks obtained from the average yield per hectare. Also the results of the proposed method outperform the results of the other ranking methods, namely VIKOR and ELECTRE, particularly in the real world example. Thus, the developed predictive mathematical model seems to provide better results for the given alternatives and can also be used for other decision-making problems.

Iso-butanol is a naturally occurring 4-carbon alcohol that can be obtained by processing organic crops like corn and sugarcane. An experimental and statistical investigation is carried out to analyze the effects of injection-pressure, timing and exhaust gas recirculation (EGR) on performance and emissions of a DI diesel engine fuelled with 40% by vol. of iso-butanol/diesel blend. Response surface methodology was used to model all measured responses like nitrogen oxides (NOx), smoke opacity, brake thermal efficiency (BTE) and brake specific fuel consumption (BSFC). Analysis of variance (ANOVA) revealed that all developed models were statistically significant. Interactive effects between injection pressure, injection timing and EGR for all blends were analyzed using response surface plots that were plotted using developed regression models. Optimization was performed using desirability approach of the RSM with an objective to minimize NOx and smoke emissions simultaneously with maximum BTE and minimum BSFC. Iso-butanol/diesel blend injected at 240bar pressure, 23°CA bTDC under 30% EGR was predicted to be optimum for this particular engine. The predicted combination was validated by confirmatory tests and the error in prediction was found to be within 4%.

This study proposes the use of Grey-Taguchi based multi-response optimization to screen suitable diesel-oxygenate blends for achieving simultaneous reduction of smoke and NOx emissions with maximum performance in a DI diesel engine with minimum number of trials. The effects of factors such as oxygenate type, its blend proportion with diesel and retarded injection timing on emission and performance variables were considered. Three popular oxygenates viz., Diethyl ether (DEE), Dimethyl carbonate (DMC) and Diglyme (DGM) were screened. Response surface models (RSM) were developed using experimental data. Taguchi’s signal-to-noise ratio approach was applied to predict optimal factor settings for all individual responses. RSM and predicted optimum factor levels were later validated by rigorous experimentation. It was found that DEE blends delivered best performance. Lowest smoke opacity was realized with DMC blends. NOx emissions were least for DEE blends. Higher DMC and DGM blends generated low HC emissions while lower DGM blends gave out low HC emissions at lower retarded injection timing. CO emissions were generally low for higher DMC blends. Smoke and NOx reducing capabilities of DGM are in between DEE and DMC. Finally it was experimentally validated that, Grey-Taguchi predicted combination of 10% DGM blend injected at 21°CA, simultaneously reduced smoke opacity(▾29.17%) and NOx emissions(▾17.4%) with best performance(▴7%) when compared to baseline diesel operation. The results indicated that Grey-Taguchi method can be effectively used to screen oxygenates suitable to achieve the set objective with minimum number of trials saving cost and time.

In the Indian agricultural sector, millions of diesel-driven pump-sets were used for irrigation purposes. These engines produce carcinogenic diesel particulates, toxic nitrogen oxides (NOx), and carbon monoxide (CO) emissions which threaten the livelihood of large population of farmers in India. The present study investigates the use of n-propanol, a less-explored high carbon bio-alcohol that can be produced by sustainable pathways from industrial and crop wastes that has an attractive opportunity for powering stationary diesel engines meant for irrigation and rural electrification. This study evaluates the use of n-propanol addition in fossil diesel by up to 30% by vol. and concurrently reports the effects of exhaust gas recirculation (EGR) on emissions of an agricultural DI diesel engine. Three blends PR10, PR20, and PR30 were prepared by mixing 10, 20, and 30% by vol. of n-propanol with fossil diesel. Results when compared to baseline diesel case indicated that smoke density reduced with increasing n-propanol fraction in the blends. PR10, PR20, and PR30 reduced smoke density by 13.33, 33.33, and 60%, respectively. NOx emissions increased with increasing n-propanol fraction in the blends. Later, three EGR rates (10, 20, and 30%) were employed. At any particular EGR rate, smoke density remained lower with increasing n-propanol content in the blends under increasing EGR rates. NOx reduced gradually with EGR. At 30% EGR, the blends PR10, PR20, and PR30 reduced NOx emissions by 43.04, 37.98, and 34.86%, respectively when compared to baseline diesel. CO emissions remained low but hydrocarbon (HC) emissions were high for n-propanol/diesel blends under EGR. Study confirmed that n-propanol could be used by up to 30% by vol. with diesel and the blends delivered lower soot density, NOx, and CO emissions under EGR.

We present Centaurus - a scalable, open source, clustering service for K-means clustering of correlated, multidimensional data. Centaurus provides users with automatic deployment via public or private cloud resources, model selection (using Bayesian information criterion), and data visualisation. We apply Centaurus to a real-world, agricultural analytics application and compare its results to the industry standard clustering approach. The application uses soil electrical conductivity (EC) measurements, GPS coordinates, and elevation data from a field to produce a 'map' of differing soil zones (so that management can be specialised for each). We use Centaurus and these datasets to empirically evaluate the impact of considering multiple K-means variants and large numbers of experiments. We show that Centaurus yields more consistent and useful clusterings than the competitive approach for use in zone-based soil decision-support applications where a 'hard' decision is required.

Knowledge of spatial variability of soil attributes within an agricultural field is critical for successful site-specific crop management. Soil sensing techniques to assess this variability on-the-go are being developed as an alternative to tedious manual soil sampling and laboratory testing. The goal of this study was to evaluate an Agitated Soil Measurement (ASM) method for integrated on-the-go mapping of soil pH, soluble potassium and residual nitrate contents using ion-selective electrodes. To implement ASM, an Integrated Agitation Chamber Module (IACM) was developed and attached to a commercial soil pH mapping implement. Precision of the tested electrodes was assessed through the root mean squared error (RMSE) and ranged from 0.10 for pK to 0.22 for pNO"3 (units represent the negative base 10 logarithm of the molar concentration of specified ions). The accuracy of the electrodes was assessed by comparing test results against reference measurements conducted in a commercial soil laboratory using the linear regression method. Average accuracy error ranged from 0.11 for pK to 0.23 for pNO"3. In a field simulation test, neither precision nor accuracy errors obtained with ASM were lower than for a previously investigated Direct Soil Measurement (DSM) method, which produced precision errors ranging from 0.11 for pH to 0.22 for pNO"3 and accuracy errors ranging from 0.12 for pNO"3 to 0.20 for pH. The coefficients of determination (r^2) of linear regressions between individual field simulation measurements and corresponding average reference measurements were 0.85-0.89 (pH), 0.50-0.54 (pK), and 0.14-0.32 (pNO"3). However, laboratory evaluation of the ASM method revealed substantially lower measurement errors and increased r^2 values when compared to the field simulation, indicating that the proposed ASM method retains the potential for improving on-the-go field mapping. Except for reduced electrode abuse and the ability to use less expensive half-cell ion-selective electrodes, physical implementation of ASM through the IACM did not bring substantial improvement over conventionally available DSM. This could be attributed to the design of the IACM and use of half-cell electrodes. Further research is necessary to improve the design of the solution-based measuring equipment and to develop an algorithm integrating on-the-go measurements with other sources of spatial data for an improved decision-making process.

Knowledge of the spatial variation of soil attributes is critical for precision agriculture. On-the-go soil sensors have been able to provide relatively high mapping density while assessing this variation. A new ion-selective electrode (ISE) based approach was developed and tested for simultaneous mapping of soil pH, residual nitrate (NO3-), and soluble potassium (K+) contents. In this article, results of laboratory experiments investigating the effects of key measurement factors on ISE performance are presented. In addition to four different soils, these factors included: soil/water ratio (SWR), quality of water used for electrode rinsing (QWR) and for ion extraction (QWE), presence of ionic strength adjuster (ISA), and solution agitation (stirring). After the targeted ion activity presented by different soils, SWR was the second most influential factor causing increased measurement variance, while the influence of QWE was only significant for pH measurements. Based on this study, the following measurement parameters were recommended: agitated purified water extraction without ISA, addition of a fixed amount of water (preferably 1:1 soil/water ratio), and use of regular (tap) water for electrode rinsing.

One of the main objectives of precision agriculture is site-specific management of agricultural inputs to increase profitability of crop production, improve product quality, and protect the environment. Information about the variability of different soil attributes within a field is essential to the decision-making process. Various soil sensors have been developed to map physical and chemical soil properties on-the-go. A method of direct soil measurement (DSM) using ion-selective electrodes (ISEs) has been applied in a commercial implement for on-the-go mapping of soil pH. This study describes a quantitative analysis of the potential for using a similar measurement procedure to simultaneously map soil pH, available potassium, nitrate-nitrogen and sodium contents. Response, calibration, precision and accuracy of the DSM were analyzed based on the results of a laboratory test. Precision of eight tested electrodes was assessed through the root mean squared error (RMSE) and ranged from 0.11 to 0.26pX (negative base 10 logarithm of specified ion activity). The accuracy of electrodes was assessed while comparing test results against reference measurements conducted in commercial soil laboratories (RMSE ranged from 0.18 to 0.37pX). The coefficients of determination (R^2) of regressions between average DSMs and corresponding reference measurements were equal to 0.93-0.96 (soil pH), 0.61-0.62 (potassium), 0.41-0.51 (nitrate-nitrogen), and 0.11 (sodium). Although DSM of pH represents a promising method for precision agriculture, the potential applicability of other ion-selective electrodes based on the results obtained declined in the order: potassium>nitrate>sodium. The primary reason for this decline was the difference in the level of variability of these soil properties in the set of soil samples used in this experiment rather than electrode errors. Performance of sodium electrodes remains questionable due the fact that only one probe was selected. Further research on integrated on-the-go mapping of soil chemical attributes using ion-selective electrodes is needed to investigate suitable application approaches.

The main objective of precision agriculture is optimized management of spatial and temporal field variability to reduce waste, increase profits and protect the quality of the environment. Knowledge of the spatial variability of soil attributes is critical for successful site-specific crop management. Different approaches to assess this variability on-the-go have been pursued through development of soil sensors. One of the methods, Agitated Soil Measurement (ASM) has been intended for integrated on-the-go mapping of soil pH, soluble potassium and residual nitrate contents using ion selective electrodes (ISE). To implement ASM, an Integrated Agitation Chamber Module (IACM) was developed and attached to a commercial soil pH mapping equipment. Based on the field simulation test, neither precision nor accuracy estimates have improved when compared to the previously investigated Direct Soil Measurement (DSM) method (RMSE ranged between 0.11 for pH to 0.22 for pNO3). However, in addition to reduced electrode abuse, laboratory evaluation of the ASM method revealed significantly lower measurement errors (RMSE ranged between 0.05 for pK to 0.13 for pNO3) for all three properties and, therefore, retained the potential for improved quality of on-the-go field mapping.

Knowledge of the spatial variability of soil attributes is critical for precision agriculture. Different approaches to assess this variability on-the-go have been taken through development of soil sensors. A new ion-selective electrode (ISE) based approach was implemented for simultaneous mapping of soil pH, residual nitrate (NO3 -) and soluble potassium (K+) contents. In this publication, the results of laboratory experiments investigating the effects of key measurement parameters are reported. These parameters include: soil-water ratio (SWR), quality of water used for electrode rinsing (QWR) and for ion extraction (QWE), presence of ionic strength adjuster (ISA) and solution agitation (stirring). Based on this study involving four different soils, the following measurement parameters were recommended: agitated purified water extraction without ISA, addition of a fixed amount of water (preferably 1:1 soil-water ratio), and regular (tap) water for electrode rinsing.

Ion-selective electrodes frequently require sufficient stabilization time that limits the achievable frequency of soil sampling. This research involves the evaluation of the response curves of ion-selective electrodes and the development of an algorithm for predicting electrode output based on the initial response. The electrode output was logged at 5 Hz. Electrode responses logged for 60 s were analyzed. The prediction of the value at 60 s was attempted from the initial 10 s output. Three combination pH electrodes with different designs of sensitive and reference parts were evaluated. The standard errors of prediction were 4.5, 2.4, and 2.6 mV (0.08, 0.04, and 0.04 pH) for electrodes 1, 2, and 3, respectively. The algorithm developed can be incorporated into a data acquisition system for mapping soil pH on-the-go.

An autonomous robot must be able to sense its environment and react appropriately in a variable environment. The University of Cincinnati Robot team is actively involved in building a small, unmanned, autonomously guided vehicle for the International Ground Robotics Contest organized by Association for Unmanned Vehicle Systems International (AUVSI) each year. The unmanned vehicle is supposed to follow an obstacle course bounded by two white/yellow lines, which are four inches thick and 10 feet apart. The navigation system for one of the University of Cincinnati’s designs, Bearcat, uses 2 CCD cameras and an image-tracking device for the front end processing of the image captured by the cameras. The three dimensional world co-ordinates were reduced to two dimensional image coordinates as a result of the transformations taking place from the ground plane to the image plane. A novel automatic calibration system was designed to transform the image co-ordinates back to world co-ordinates for navigation purposes. The purpose of this paper is to simplify this tedious calibration using an artificial neural network. Image processing is used to automatically detect calibration points. Then a back projection neural algorithm is used to learn the relationships between the image coordinates and three-dimensional coordinates. This transformation is the main focus of this study. Using these algorithms, the robot built with this design is able to track and follow the lines successfully.

Secure remote access with inter-operatability for operating a robot can be successfully achieved using the web services provided in the .NET framework. The complete design of the machine discussed in this paper is made on the .NET framework. The server which operates the robot is configured to IIS. The algorithm for obstacle detection is coded on a different server using the .NET framework. By using web services, the robot can be accessed by other servers. These web services are consumed by the server on which the robot executes. A proxy is created on this server. The whole control is given in the form of a series of web pages which can be accessed by any web browser. However in order to input parameters and control the robot, authentication is required. The user provides authentication credentials which are matched with the existing information on the data base. After authentication, the user proceeds further to control the robot. The security and reliability of remote access is provided by the components that come with the web services namely, SOAP, WSDL and Proxy.

Measurement of quality and segregation of wine grapes during harvest is a growing need for producers and wineries alike. The commonly used quality indicators for wine grapes are anthocyanin (mg/g) and brix (degrees) contents. Geo-referenced field samples of wine grapes were measured for anthocyanin and brix using a portable near-infrared (NIR) spectrometer. Data was collected from 437 sampling points in a 45 acre block of vineyard in the Central Valley of California. The quality indicators were subject to geo-spatial modeling for the distribution of the spatial variability of wine quality across the field. Subsequently, the anthocyanin dataset was utilized to produce a quality zone map of ‘high’ and ‘low’ for the vineyard while the brix dataset was utilized to determine the timing of the harvest. The anthocyanin concentration used to differentiate between high and low quality was above or below 0.87 mg anthocyanin/g fruit respectively. Based on this quality map, a modified commercial mechanical grape harvester was utilized for segregation of wine grapes on-the-go. Three 40 tons lots of wine grapes representing the standard (average) field blend, high anthocyanin and low anthocyanin were differentially harvested. These wine grapes were fermented separately were subjected to analytical and taste panel analysis.