Fertilization is an essential element in plant cultivation. Supplying the right amounts of nutrients allows plants to grow and develop. Due to the rising price of mineral fertilizers, other fertilizers and soil conditioners are growing in importance. One of these is the digestate produced in agricultural biogas plants. Due to its properties, the digestate can be used directly as a fertilizer. In this case, the effects of application can both change the soil environment and directly affect plant growth. Physical, biological, and thermal transformations can also produce products based on the digestate or its fractions, which can be successfully used for fertilizer purposes. Among other things, this paper discusses the production and use of composts, biocarbon, and/or fertilizer granules from the solid fraction of the digestate. Numerous scientific studies, including the authors’ own research in this article, indicate that digestate can be successfully used as fertilizer, both without processing and with selected methods of treatment. However, further research is needed—especially on the diversity of raw materials used for biogas production and their effects on the composition and performance of the digestate. In addition, research should continue on the processing of digestate into specific products, depending on the needs of soils and plants.
The aim of this paper is to present, in theoretical and application terms, artificial neural networks (ANNs) as a method of estimating the product cost. The first part of the article reviews the methods used to estimate the product cost. The basic approaches to the problem of product cost estimation, presented by various authors, were described. In the second part an empirical study using artificial neural networks was conducted. Two research methods were used in this paper: literature analysis and empirical research carried out in the form of an extensive case study. The test object is a new generation induction motor. The main research problem of the article is the modelling of artificial neural networks for the estimation process of product costs with advanced production technology. The test procedures focus on the application aspects. The conclusions discuss the usefulness and advantages of using ANN models in estimating the costs of products.
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