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Biorefining and biorefineries are the future of industry and energy. It is still a long way to complete its implementation, but small biorefineries focused mainly on the production of fuels and energy are more and more frequent in rural areas and large areas located near big cities in which, in addition to fuels and energy, various organic substances of high market value are also produced. In order to optimize biogas production and to control methane fermentation processes, fast and accurate identification of carboxylic acid concentrations, including propionic acid as a precursor to acetic acid, is needed. In this study, a process quality control method was developed to evaluate the propionic acid content of an aqueous solution from the fermentation mass. The proposed methodology is based on near infrared spectroscopy with multivariate analysis and stochastic metamodeling with a denoising procedure based on proper orthogonal decomposition (POD). The proposed methodology uses the Bayesian theory, which provides additional information on the magnitude of the correlation between state and control variables. The calibration model was, therefore, constructed by using Gaussian Processes (GP) to predict propionic acid content in the aqueous solution using an NIR-Vis spectrophotometer. The design of the calibration model was based on absorbance spectra and calculation data from selected wavelength ranges from 305 nm to 2210 nm. Measurement data were first denoised and truncated to build a fast and reliable metamodel for precise identification of the acid content of an aqueous solution at a concentration from 0 to 5.66%. The mean estimation error generated by the metamodel does not exceed 0.7%.

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Enhancing digital and precision agriculture is currently inevitable to overcome the economic and environmental challenges of the agriculture in the 21st century. The purpose of this study was to generate and compare management zones (MZ) based on the Sentinel-2 satellite data for variable rate application of mineral nitrogen in wheat production, calculated using different remote sensing (RS)-based models under varied soil, yield and crop data availability. Three models were applied, including (1) a modified “RS- and threshold-based clustering”, (2) a “hybrid-based, unsupervised clustering”, in which data from different sources were combined for MZ delineation, and (3) a “RS-based, unsupervised clustering”. Various data processing methods including machine learning were used in the model development. Statistical tests such as the Paired Sample T-test, Kruskal–Wallis H-test and Wilcoxon signed-rank test were applied to evaluate the final delineated MZ maps. Additionally, a procedure for improving models based on information about phenological phases and the occurrence of agricultural drought was implemented. The results showed that information on agronomy and climate enables improving and optimizing MZ delineation. The integration of prior knowledge on new climate conditions (drought) in image selection was tested for effective use of the models. Lack of this information led to the infeasibility of obtaining optimal results. Models that solely rely on remote sensing information are comparatively less expensive than hybrid models. Additionally, remote sensing-based models enable delineating MZ for fertilizer recommendations that are temporally closer to fertilization times.