Monitoring of the primary drying of a lyophilization process in vials

An innovative and modular system (LyoMonitor) for monitoring the primary drying of a lyophilization process in vials is illustrated: it integrates some commercial devices (pressure gauges, moisture sensor and mass spectrometer), an innovative balance and a manometric temperature measurement system based on an improved algorithm (DPE) to estimate sublimating interface temperature and position, product temperature profile, heat and mass transfer coefficients. A soft-sensor using a multipoint wireless thermometer can also estimate the previous parameters in a large number of vials. The performances of the previous devices for the determination of the end of the primary drying are compared. Finally, all these sensors can be used for control purposes and for the optimization of the process recipe; the use of DPE in a control loop will be shown as an exampl

Sensitivity of water stress in a two-layered sandy grassland soil to variations in groundwater depth and soil hydraulic parameters

Monitoring and modelling tools may improve irrigation strategies in precision agriculture. We used non-invasive soil moisture monitoring, a crop growth and a soil hydrological model to predict soil water content fluctuations and crop yield in a heterogeneous sandy grassland soil under supplementary irrigation. The sensitivity of the soil hydrological model to hydraulic parameters, water stress, crop yield and lower boundary conditions was assessed after integrating models. Free drainage and incremental constant head conditions were implemented in a lower boundary sensitivity analysis. A time-dependent sensitivity analysis of the hydraulic parameters showed that changes in soil water content are mainly affected by the soil saturated hydraulic conductivity K-s and the Mualem-van Genuchten retention curve shape parameters n and alpha. Results further showed that different parameter optimization strategies (two-, three-, four- or six-parameter optimizations) did not affect the calculated water stress and water content as significantly as does the bottom boundary. In this case, a two-parameter scenario, where K-s was optimized for each layer under the condition of a constant groundwater depth at 135-140 cm, performed best. A larger yield reduction, and a larger number and longer duration of stress conditions occurred in the free drainage condition as compared to constant boundary conditions. Numerical results showed that optimal irrigation scheduling using the aforementioned water stress calculations can save up to 12-22 % irrigation water as compared to the current irrigation regime. This resulted in a yield increase of 4.5-6.5 %, simulated by the crop growth model

Freeze-drying modeling and monitoring using a new neuro-evolutive technique

This paper is focused on the design of a black-box model for the process of freeze-drying of pharmaceuticals. A new methodology based on a self-adaptive differential evolution scheme is combined with a back-propagation algorithm, as local search method, for the simultaneous structural and parametric optimization of the model represented by a neural network. Using the model of the freeze-drying process, both the temperature and the residual ice content in the product vs. time can be determine off-line, given the values of the operating conditions (the temperature of the heating shelf and the pressure in the drying chamber). This makes possible to understand if the maximum temperature allowed by the product is trespassed and when the sublimation drying is complete, thus providing a valuable tool for recipe design and optimization. Besides, the black box model can be applied to monitor the freeze-drying process: in this case, the measurement of product temperature is used as input variable of the neural network in order to provide in-line estimation of the state of the product (temperature and residual amount of ice). Various examples are presented and discussed, thus pointing out the strength of the too

Root Zone Sensors for Irrigation Management in Intensive Agriculture

Crop irrigation uses more than 70% of the world’s water, and thus, improving irrigation efficiency is decisive to sustain the food demand from a fast-growing world population. This objective may be accomplished by cultivating more water-efficient crop species and/or through the application of efficient irrigation systems, which includes the implementation of a suitable method for precise scheduling. At the farm level, irrigation is generally scheduled based on the grower’s experience or on the determination of soil water balance (weather-based method). An alternative approach entails the measurement of soil water status. Expensive and sophisticated root zone sensors (RZS), such as neutron probes, are available for the use of soil and plant scientists, while cheap and practical devices are needed for irrigation management in commercial crops. The paper illustrates the main features of RZS’ (for both soil moisture and salinity) marketed for the irrigation industry and discusses how such sensors may be integrated in a wireless network for computer-controlled irrigation and used for innovative irrigation strategies, such as deficit or dual-water irrigation. The paper also consider the main results of recent or current research works conducted by the authors in Tuscany (Italy) on the irrigation management of container-grown ornamental plants, which is an important agricultural sector in Italy

Data assimilation of in situ soil moisture measurements in hydrological models: first annual doctoral progress report, work plan and achievements

Water scarcity and the presence of water of good quality is a serious public concern since it determines the availability of water to society. Water scarcity especially in arid climates and due to extreme droughts related to climate change drive water use technologies such as irrigation to become more efficient and sustainable. Plant root water and nutrient uptake is one of the most important processes in subsurface unsaturated flow and transport modeling, as root uptake controls actual plant evapotranspiration, water recharge and nutrient leaching to the groundwater, and exerts a major influence on predictions of global climate models. To improve irrigation strategies, water flow needs to be accurately described using advanced monitoring and modeling. Our study focuses on the assimilation of hydrological data in hydrological models that predict water flow and solute (pollutants and salts) transport and water redistribution in agricultural soils under irrigation. Field plots of a potato farmer in a sandy region in Belgium were instrumented to continuously monitor soil moisture and water potential before, during and after irrigation in dry summer periods. The aim is to optimize the irrigation process by assimilating online sensor field data into process based models. Over the past year, we demonstrated the calibration and optimization of the Hydrus 1D model for an irrigated grassland on sandy soil. Direct and inverse calibration and optimization for both heterogeneous and homogeneous conceptualizations was applied. Results show that Hydrus 1D closely simulated soil water content at five depths as compared to water content measurements from soil moisture probes, by stepwise calibration and local sensivity analysis and optimization the Ks, n and α value in the calibration and optimization analysis. The errors of the model, expressed by deviations between observed and modeled soil water content were, however, different for each individual depth. The smallest differences between the observed value and soil-water content were attained when using an automated inverse optimization method. The choice of the initial parameter value can be optimized using a stepwise approach. Our results show that statistical evaluation coefficients (R2, Ce and RMSE) are suitable benchmarks to evaluate the performance of the model in reproducing the data. The degree of water stress simulated with Hydrus 1D suggested to increase irrigation at least one time, i.e. at the beginning of the simulation period and further distribute the amount of irrigation during the growing season, instead of using a huge amount of irrigation later in the season. In the next year, we will further look for to the best method (using soft data and methods for instance PTFs, EMI, Penetrometer) to derive and predict the spatial variability of soil hydraulic properties (saturated hydraulic conductivity) of the soil and link to crop yield at the field scale. Linear and non-linear pedotransfer functions (PTFs) have been assessed to predict penetrometer resistance of soils from their water status (matric potential, ψ and degree of saturation, S) and bulk density, ρb, and some other soil properties such as sand content, Ks etc. The geophysical EMI (electromagnetic induction) technique provides a versatile and robust field instrument for determining apparent soil electrical conductivity (ECa). ECa, a quick and reliable measurement, is one of ancillary properties (secondary information) of soil, can improve the spatial and temporal estimation of soil characteristics e.g., salinity, water content, texture, prosity and bulk density at different scales and depths. According to previous literature on penetrometer measurements, we determined the effective stress and used some models to find the relationships between soil properties, especially Ks, and penetrometer resistance as one of the prediction methods for Ks. The initial results obtained in the first yearshowed that a new data set would be necessary to validate the results of this part. In the third year, quasi 3D-modelling of water flow at the field scale will be conducted. In this modeling set -up, the field will be modeled as a collection of 1D-columns representing the different field conditions (combination of soil properties, groundwater depth, root zone depth). The measured soil properties are extrapolated over the entire field by linking them to the available spatially distributed data (such as the EMI-images). The data set of predicted Ks and other soil properties for the whole field constructed in the previous steps will be used for parameterising the model. Sensitivity analysis ‘SA’ is essential to the model optimization or parametrization process. To avoid overparameterization, the use of global sensitivity analysis (SA) will be investigated. In order to include multiple objectives (irrigation management parameters, costs, …) in the parameter optimization strategy, multi-objective techniques such as AMALGAM have been introduced. We will investigate multi-objective strategies in the irrigation optimization

Harmonization of space-borne infra-red sensors measuring sea surface temperature

Sea surface temperature (SST) is observed by a constellation of sensors, and SST retrievals are commonly combined into gridded SST analyses and climate data records (CDRs). Differential biases between SSTs from different sensors cause errors in such products, including feature artefacts. We introduce a new method for reducing differential biases across the SST constellation, by reconciling the brightness temperature (BT) calibration and SST retrieval parameters between sensors. We use the Advanced Along-Track Scanning Radiometer (AATSR) and the Sea and Land Surface Temperature Radiometer (SLSTR) as reference sensors, and the Advanced Very High Resolution Radiometer (AVHRR) of the MetOp-A mission to bridge the gap between these references. Observations across a range of AVHRR zenith angles are matched with dual-view three-channel skin SST retrievals from the AATSR and SLSTR. These skin SSTs act as the harmonization reference for AVHRR retrievals by optimal estimation (OE). Parameters for the harmonized AVHRR OE are iteratively determined, including BT bias corrections and observation error covariance matrices as functions of water-vapor path. The OE SSTs obtained from AVHRR are shown to be closely consistent with the reference sensor SSTs. Independent validation against drifting buoy SSTs shows that the AVHRR OE retrieval is stable across the reference-sensor gap. We discuss that this method is suitable to improve consistency across the whole constellation of SST sensors. The approach will help stabilize and reduce errors in future SST CDRs, as well as having application to other domains of remote sensing

Use of high-dimensional spectral data to evaluate organic matter, reflectance relationships in soils

Recent breakthroughs in remote sensing technology have led to the development of a spaceborne high spectral resolution imaging sensor, HIRIS, to be launched in the mid-1990s for observation of earth surface features. The effects of organic carbon content on soil reflectance over the spectral range of HIRIS, and to examine the contributions of humic and fulvic acid fractions to soil reflectance was evaluated. Organic matter from four Indiana agricultural soils was extracted, fractionated, and purified, and six individual components of each soil were isolated and prepared for spectral analysis. The four soils, ranging in organic carbon content from 0.99 percent, represented various combinations of genetic parameters such as parent material, age, drainage, and native vegetation. An experimental procedure was developed to measure reflectance of very small soil and organic component samples in the laboratory, simulating the spectral coverage and resolution of the HIRIS sensor. Reflectance in 210 narrow (10 nm) bands was measured using the CARY 17D spectrophotometer over the 400 to 2500 nm wavelength range. Reflectance data were analyzed statistically to determine the regions of the reflective spectrum which provided useful information about soil organic matter content and composition. Wavebands providing significant information about soil organic carbon content were located in all three major regions of the reflective spectrum: visible, near infrared, and middle infrared. The purified humic acid fractions of the four soils were separable in six bands in the 1600 to 2400 nm range, suggesting that longwave middle infrared reflectance may be useful as a non-destructive laboratory technique for humic acid characterization

Near infrared and Raman spectroscopy for the in-process monitoring of pharmaceutical production processes

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Using Canopies indices to Quantify the Economic optimum nitrogen rate in Spring Wheat

In-season N applications to spring wheat (Triticum aestivum L.) may increase profits and improve N fertilizer accuracy. The objectives were to develop a calibration tool employing normalized difference vegetative index (NDVI) and SPAD 502 chlorophyll meter (SPAD) measurements for calculating the differential from the economic optimum N rate (dEONR) at growth stages Z22, Z24, and Z31 to Z39 and provide N rate algorithms for use in applying N fertilizer at a variable rate. Sensing was conducted trials over 3 yr encompassing 10 site-years across Southeastern Buenos Aires Province, Argentina. The relationship between sensor indices and dEONR was evaluated by fitting quadratic plateau (QP) regression models. Statistically significant QP models were determined at the Z24, Z31, and Z39 growth stages. Relative SPAD (rSPAD) and relative NDVI (rNDVI) reduced variation and improved the calibration of measured N stress with the dEONR. For Z31 and Z39, the rSPAD had the best goodness of fit statistics when compared to rNDVI [adjusted R2 (adjR2)= 0.67 and 0.57 at Z31 and 0.68 and 0.52 at Z39, respectively]. However, adjustment at Z24 was higher for rNDVI (adjR2 = 0.53 and 0.61 for rSPAD and rNDVI, respectively). A single QP model to estimate the dEONR with 58% confidence was adjusted for the Z31 and Z39 growth stages. This indicates that the same calibration for N rate determination based on rSPAD or rNDVI values can be used during stem elongation in spring wheat. This model can be used as an N rate algorithm for applying N fertilizer in-season.Fil: Reussi Calvo, Nahuel Ignacio. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Sainz Rozas, Hernan Rene. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: Echeverria, Hernan Eduardo. Instituto Nacional de Tecnología Agropecuaria; Argentina. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; ArgentinaFil: Diovisalvi, Nadia Rosalia. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

The Use of Chlorophyll Meters to Assess Crop N Status and Derivation of Sufficiency Values for Sweet Pepper

Chlorophyll meters are promising tools for improving the nitrogen (N) management of vegetable crops. To facilitate on-farm use of these meters, sufficiency values that identify deficient and sufficient crop N status are required. This work evaluated the ability of three chlorophyll meters (SPAD-502, atLEAF+, and MC-100) to assess crop N status in sweet pepper. It also determined sufficiency values for optimal N nutrition for each meter for pepper. The experimental work was conducted in a greenhouse, in Almería, Spain, very similar to those used for commercial production, in three different crops grown with fertigation. In each crop, there were five treatments of different N concentration in the nutrient solution, applied in each irrigation, ranging from a very deficient to very excessive N supply. In general, chlorophyll meter measurements were strongly related to crop N status in all phenological stages of the three crops, indicating that these measurements are good indicators of the crop N status of pepper. Sufficiency values determined for each meter for the four major phenological stages were consistent between the three crops. This demonstrated the potential for using these meters with sufficiency values to improve the N management of commercial sweet pepper crops