627 research outputs found
Demand-side management via optimal production scheduling in power-intensive industries: The case of metal casting process
The increasing challenges to the grid stability posed by the penetration of
renewable energy resources urge a more active role for demand response programs
as viable alternatives to a further expansion of peak power generators. This
work presents a methodology to exploit the demand flexibility of
energy-intensive industries under Demand-Side Management programs in the energy
and reserve markets. To this end, we propose a novel scheduling model for a
multi-stage multi-line process, which incorporates both the critical
manufacturing constraints and the technical requirements imposed by the market.
Using mixed integer programming approach, two optimization problems are
formulated to sequentially minimize the cost in a day-ahead energy market and
maximize the reserve provision when participating in the ancillary market. The
effectiveness of day-ahead scheduling model has been verified for the case of a
real metal casting plant in the Nordic market, where a significant reduction of
energy cost is obtained. Furthermore, the reserve provision is shown to be a
potential tool for capitalizing on the reserve market as a secondary revenue
stream
Optical properties of developing pip and stone fruit reveal underlying structural changes
Analyzing the optical properties of fruits represents a powerful approach for non-destructive observations of fruit development. With classical spectroscopy in the visible and near-infrared wavelength ranges, the apparent attenuation of light results from its absorption or scattering. In horticultural applications, frequently, the normalized difference vegetation index (NDVI) is employed to reduce the effects of varying scattering properties on the apparent signal. However, this simple approach appears to be limited. In the laboratory, with time-resolved reflectance spectroscopy, the absorption coefficient, μa, and the reduced scattering coefficient, μs′, can be analyzed separately. In this study, these differentiated optical properties were recorded (540-940 nm), probing fruit tissue from the skin up to 2 cm depth in apple (Malus × domestica 'Elstar') and plum (Prunus domestica 'Tophit plus') harvested four times (65-145 days after full bloom). The μa spectra showed typical peak at 670 nm of the chlorophyll absorption. The μs′ at 670 nm in apple changed by 14.7% (18.2-15.5 cm-1), while in plum differences of 41.5% (8.5-5.0 cm-1) were found. The scattering power, the relative change of μs′, was zero in apple, but enhanced in plum over the fruit development period. This mirrors more isotropic and constant structures in apple compared with plum. For horticultural applications, the larger variability in scattering properties of plum explains the discrepancy between commercially assessed NDVI values or similar indices and the absolute μa values in plum (R < 0.05), while the NDVI approach appeared reasonable in apple (R ≥ 0.80)
Probabilistic electric load forecasting through Bayesian Mixture Density Networks
Probabilistic load forecasting (PLF) is a key component in the extended
tool-chain required for efficient management of smart energy grids. Neural
networks are widely considered to achieve improved prediction performances,
supporting highly flexible mappings of complex relationships between the target
and the conditioning variables set. However, obtaining comprehensive predictive
uncertainties from such black-box models is still a challenging and unsolved
problem. In this work, we propose a novel PLF approach, framed on Bayesian
Mixture Density Networks. Both aleatoric and epistemic uncertainty sources are
encompassed within the model predictions, inferring general conditional
densities, depending on the input features, within an end-to-end training
framework. To achieve reliable and computationally scalable estimators of the
posterior distributions, both Mean Field variational inference and deep
ensembles are integrated. Experiments have been performed on household
short-term load forecasting tasks, showing the capability of the proposed
method to achieve robust performances in different operating conditions.Comment: 56 page
Clinical trial of time-resolved scanning optical mammography at 4 wavelengths between 683 and 975 nm
The first time-resolved optical mammograph operating beyond 900 nm (683, 785, 913, and 975 nm) is presently being used in a clinical trial to test the diagnostic potential of the technique in detecting and characterizing breast lesions. Between November 2001 and October 2002, 101 patients with malignant and benign lesions were analyzed retrospectively. Scattering plots, as derived from a homogeneous model, and late gated intensity images, to monitor spatial changes in the absorption properties, are routinely used. The intensity images available at four wavelengths provide sensitivity to the main tissue constituents (oxy- and deoxyhemoglobin, water, and lipids), in agreement with expected tissue composition and physiology, while the scattering plots mirror structural changes. Briefly, tumors are usually identified due to the strong blood absorption at short wavelengths, cysts to the low scattering, and fibroadenomas to low absorption at 913 nm and high at 975 nm, even though the optical features of fibroadenomas seem not to be uniquely defined. The effectiveness of the technique in localizing and discriminating different lesion types is analyzed as a function of various parameters (lesion size, compressed breast thickness, and breast parenchymal pattern).
Time-resolved reflectance spectroscopy as a management tool for late-maturing nectarine supply chain
The absorption coefficient of the fruit flesh at 670 nm (mu(a)), measured at harvest by time-resolved reflectance spectroscopy (TRS) is a good maturity index for early nectarine cultivars. A kinetic model has been developed linking the mu(a), expressed as the biological shift factor to softening during ripening. This allows shelf life prediction for individual fruit from the value of mu(a) at harvest and the fruit categorization into predicted softening and usability classes. In this work, the predictive capacity of a kinetic model developed using mu(a) data at harvest and firmness data within 1-2 d after harvest for a late maturing nectarine cultivar ('Morsiani 90') was tested for prediction and classification ability. Compared to early maturing cultivars, mu(a) at harvest had low values and low variability, indicating advanced maturity, whereas firmness was similar. Hence, fruit were categorized into six usability classes (from 'transportable-hard' to 'ready-to-eat-very soft') basing on mu(a) limits established analyzing firmness data in shelf life after harvest. The model was tested by comparing the predicted firmness and class of usability to the actual ones measured during ripening and its performance compared to that of models based on data during the whole shelf life at 20 degrees C after harvest and after storage at 0 degrees C and 4 degrees C. The model showed a classification ability very close to that of models based on data of the whole shelf life, and was able to correctly segregate the 'ready-to-eat-transportable', 'transportable' and 'transportable-hard' classes for ripening at harvest and after storage at 0 degrees C, and the 'ready-to-eat-very soft' and 'ready-to-eat-soft' classes for ripening after storage at 4 degrees C, with lower performance of models for fruit after storage at 4 degrees C respect to those of the other two ripening
Non-destructive analysis of anthocyanins in cherries by means of Lambert-Beer and multivariate regression based on spectroscopy and scatter correction using time-resolved analysis
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Validation of time domain near infrared spectroscopy in muscle measurements: Effect of a superficial layer
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