Category: AUTOMATICA AND INFORMATICS 1, 2023

AUTOMATICA AND INFORMATICS

ISSN 0861-7562 Print,ISSN 2683-1279 Online

Year LVI
No. 1/2023

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Contents

CONTENTS

automatica
M. Hadjiski. 30th Anniversary Symposium on Control of Energy, Industrial and Environmental Systems

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Key Words: Control praxis; ecology; energy; engineering; industrial automation; innovation.

Abstract. The presented overview represents a general view of the successfully held 30th edition of the International Symposium on Control of Energy, Industrial and Environmental Systems. This was not a narrowly specialized forum, it was a search for a possible way to build a bridge between theoretical achievements and their practical implementation in industrial automation. The main automation engineering companies and the relevant organizational structures in industry and business in Bulgaria have invariably been active participants and sponsors of the symposium. The total number of reports and company presentations published in the pro- ceedings of the symposium exceeded 500. During the three decades of its existence the symposium was constantly evolving following the rapid progress of theoretical achievements, new technical devices and advanced software, emerging innovative technologies based on Big Data and Artificial Intelligence. The symposium was a place for productive discussions, exchange of experience and mutual enrichment of specialists with different profiles. It was a suitable environment for creating new contacts, which in many cases grew into long-term business cooperation. The symposium was a forum for popularizing the scientific and practical achievements of industrial automation in Bulgaria. It was often a place for first appearance and publication of young specialists from scientific circles and industry.

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modeling of energy systems
I. Simeonov, N. Pan, H. Wang, Y. Tian, E. Chorukova, N. Christov. Energy Yields Comparative Study for One-Stage and Two-Stage Anaerobic Digestion Processes

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Key Words: Anaerobic digestion; one-stage anaerobic digestion processes; two-stage anaerobic digestion processes; mathematical modeling; static characteristics; biogas yields; energy yield.

Abstract. Compared to traditional one-stage anaerobic digestion processes (OSAD) with biomethane production, this paper focuses on the study of energy yield of the two-stage anaerobic digestion processes (TSAD), which are able to produce simultaneously biohydrogen and biomethane. In TSAD, relatively fast growing acidogens and H 2-producing bacteria are developed in the first-stage hydrogenic bioreactor and are involved in the production of volatile fatty acides (VFA) and H2. On the other hand, the slow growing acetogens and methanogens are developed in the second-stage methanogenic bioreactor, in which the produced VFA are further converted to CH4 and CO2. This separation allows to optimize physico-chemical parameters for both groups of microorganisms which are not the same. Using mathematical models (mass balance type) developed by our team, theoretical comparative analysis of the energy yield from one-stage and two-stage anaerobic digestion systems is performed. Transforming the differential equations of these balance models, some algebraic equations called static characteristics for both bioreactors were obtained. They represent dependencies of the main process variables from the control variable (dilution rate). On the basis of theese results, the theoretical maximal values of the corresponding enery carriers (hydrogen and methane) yields can be found for different values of the inlet organics (perturbation). The possible maximal biohydrogen and biomethane yields and the overall energy production are calculated by the static characteristics and extremum points of both systems. From the performed analytical and simulation studies, it can be seen that the energy obtained with TSAD is from 32% to 48% greater compared to OSAD under similar conditions (depending on the concentration of the incoming organics).

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intelligent systems
A. V. Atanasov, D. Pilev, F. Tomova. Bimodal System for Emotion Recognition Based on Deep Neural Networks

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Key Words: Online Learning; Deep Neural Networks; Face Recognition; Facial Emotions Recognition; Python.

Abstract. Current study presents development of bimodal system for Facial Emotion Recognition (FER) and Body Gestures Emotion Recognition (BER). The system is based on two Deep Learning Neural Networks (DNN) each one responsible for the recognition of the emotion of the face or the body. The use of the combination of two neural networks has an amplifying synergistic effect, which increases by about 10% the accuracy of the results (recognized emotionс) compared to those of the individual DNN. The selection of pre-trained DNN models for facial and body emotions recognition is based on two authors’ papers, in which detailed analysis of the DNN for FER and BER has been done. Therefore in current study a brief information about selected DNN models is provided, as well information about specific dataset used for training selected DNNs. Verification of the bimodal system is done using our private dataset.

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intelligent systems
S. Yordanov, G. Mihalev, S. Ivanov, H. Stoycheva. Intelligent Management System for Collection of Solid Waste

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Key Words: Waste collection; smart city; Internet of Things (IoT); intelligent transportation systems; surveillance systems.

Abstract. The paper presents the structure of an intelligent integrated system for managing the collection of solid waste in urban and suburban environments. The system can automatically maintain the box level and send information to the waste collection truck. The technologies used in the proposed system are good needed to provide real monitoring and management of waste collection processes and to obtain a green environment

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informatics
D. Parvanov, P. Tomov, T. Balabanov. Fine Tuning of LibreOffice Calc NLP Solver for Multi-Objective Optimization

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Key Words: LibreOffice Calc; multi-objective optimization; NLP Solver.

Abstract. There is a common difference between single-objective optimization and multi-objective optimization. In the first case, there is only a single value as a result of the optimization. In the second case, there is a set of solutions called Pareto-optimal solutions. Single-objective solvers are giving only a single value as a result, even for multimodal functions. Because of this single-objective solver is not proper for multi-objective problems. Through additional adaptation, a single-objective solver is possible to start multiple times. Taking the results of multiple starts, the Pareto front is marked. When the solver is a metaheuristic, the front itself is difficult to achieve. With fine-tuning of the solver’s parameters, the solutions can be as close as possible.

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