Dramatic changes in the global energy market due to COVID-19 pandemic, the OPEC+ agreement, and increasing rates of green energy share in the world community have brought nega-tive effects on the oil sector. In the long term, oil will reduce its importance as an energy resource, but for many years, it will continue to play a significant role in the world of energy. The oil industry has huge potential in terms of technical expertise, management, and financial resources to reduce its greenhouse emissions and ensuring an affordable availability of predictable energy. However, nowadays this sector has lost investing attractiveness. It is an interdisciplinary problem with a so-lution at the intersection of different stakeholders’ interests. The article is a review one and devoted to the issue of the implementation a public-private partnership (PPP) as a key tool that allows the use of the state and the business’ available resources to achieve the sector’s sustainable development and investment attractiveness. Research and analysis of PPP were based on foreign and domestic literature, using classification and generalization methods, retrospective and critical analysis. This paper contains identified groups of drivers, constraints, and recommendations for further successful PPP implementation in the Russian case.

In this article actual topics concerned with mechanical properties of bulk materials, usage of computer vision and artificial neural networks in this research are discussed. The main principles of the system for analysis of bulk materials mechanical characteristics are described. Bulk material outflow behaviour with predefined parameters (particles shapes and radius, coefficients of friction, etc.) was modelled. The outflow was modelled from the calibrated conical funnel. Obtained dependencies between mechanical characteristics and pile geometrical properties are represented as diagrams and graphs.

This article is devoted to the prediction of the residual life based on an estimate of the technical state of the induction motor. The proposed system allows to increase the accuracy and completeness of diagnostics by using an artificial neural network (ANN), and also identify and predict faulty states of an electrical equipment in dynamics. The results of the proposed system for estimation the technical condition are probability technical state diagrams and a quantitative evaluation of the residual life, taking into account electrical, vibrational, indirect parameters and detected defects. Based on the evaluation of the technical condition and the prediction of the residual life, a decision is made to change the control of the operating and maintenance modes of the electric motors.