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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...

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.

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.

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.

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.