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The article presents a software model of complex processes of small-scale ore raw material processing based on a trainable tree of fuzzy logical inference systems. Processing of such raw materials not only provides a valuable end product, such as yellow phosphorus, but also helps to solve the problem of ore waste disposal, the fine fractions of which create significant environmental damage to the territories adjacent to mining and processing plants. The technological system of small-scale ore raw material processing consists of energy-intensive units, so even a slight relative reduction in resource and energy costs leads to large savings in absolute figures. Such a reduction can be achieved by optimizing the control of units, the synthesis of which requires the availability of process models, so improving the methods and tools for modeling is an urgent research task. A feature of the proposed model is that its inputs are not only variables describing resource transformations, but also variables reflecting the energy costs of individual technological units. This allows using the model to calculate the energy and resource efficiency of small-scale ore raw material processing. The hierarchical structure of the fuzzy-logical tree is capable of reflecting the interrelationship of processes of various natures accompanying the processing of small-scale raw materials, and also contributes to increasing the efficiency of its training by dividing the feature space of large dimension into several groups on which individual tree nodes are trained. The program developed in the MatLab environment and implementing the proposed model showed high regression accuracy on a synthetic set of input data, which may indicate the feasibility of using the proposed model in problems of optimizing control systems for processing small-scale raw materials according to the criterion of energy and resource efficiency.