<p>У оквиру дисертације, развијен је модел управљања биоразградивим отпадом из комерцијалних објеката за Град Нови Сад а који се може применити и на друге градове у Србији и земљама у окружењу. Модел је развијен на бази поређења три модела која су сагледавана у различитим деловима Европе и Америци и идентификације свих комерцијалних објеката на територији Града Новог Сада, по врсти услуге коју пружају, величини, броју запослених, мерењем састава и количине отпада на репрезентативном броју комерцијалних објеката за храну и пиће и хотели. Мерења су вршена током седам дана узастопно, три пута у току различитих годишњих доба, мерењем целе количине генерисаног отпада и утврђивањем његовог састава. Предвиђена су четири могућа сценарија која је могуће применити на систем управљања отпадом.</p> / <p>U okviru disertacije, razvijen je model upravljanja biorazgradivim otpadom iz komercijalnih objekata za Grad Novi Sad a koji se može primeniti i na druge gradove u Srbiji i zemljama u okruženju. Model je razvijen na bazi poređenja tri modela koja su sagledavana u različitim delovima Evrope i Americi i identifikacije svih komercijalnih objekata na teritoriji Grada Novog Sada, po vrsti usluge koju pružaju, veličini, broju zaposlenih, merenjem sastava i količine otpada na reprezentativnom broju komercijalnih objekata za hranu i piće i hoteli. Merenja su vršena tokom sedam dana uzastopno, tri puta u toku različitih godišnjih doba, merenjem cele količine generisanog otpada i utvrđivanjem njegovog sastava. Predviđena su četiri moguća scenarija koja je moguće primeniti na sistem upravljanja otpadom.</p> / <p>Within the framework of the dissertation, a model of biodegradable waste management from commercial facilities for the City of Novi Sad has been developed, which can be applied to other cities in Serbia and surrounding countries. The model was developed on the basis of comparing three models that were examined in different parts of Europe and America and the identification of all commercial facilities in the territory of the City of Novi Sad, by type of service they provide, the size, the number of employees, the measurement of the composition and quantity of waste on a representative number of commercial facilities for food and drink and hotels. Measurements were carried out over seven consecutive days, three times during different seasons, measuring the entire amount of generated waste and determining its composition. There are four possible scenarios that can be applied to the waste management system.</p>

The current method for estimating wood failure is highly subjective. Various techniques have been proposed to improve the current protocol, but none have succeeded. This research aims to use deep learning/semantic segmentation using SegNet architecture to estimate wood failure in four types of three-ply plywood from mechanical shear strength specimens. We trained and tested our approach on custom and commercial plywood with bio-based and phenol-formaldehyde adhesives. Shear specimens were prepared and tested. Photographs of 255 shear bonded areas were taken. Forty photographs were used to solicit visual estimates from five human evaluators, and the remaining photographs were used to train the machine learning models. Twelve models were trained with the combination of four image sizes and three dataset splits. In comparison to visual estimates, the model trained on 512 × 512 image size with 90/10 dataset split had a mean absolute error (MAE) of 6%, which was the best among the literature.

This diploma thesis deals with the creation of a computation driver model. In the first part, there is an overview on driver models for longitudinal and lateral control. Next, driving maneuvres that could be selected for testing of driver model are described. In the practical part, there is created a computational driver model, whose task is to follow required path. The resulting model is tested on three driving maneuvers - steady turning, moose test and slalom. Finally, this model is tested on the passage of a real track. For all these tracks, a comparison is made and the success of the model is evaluated.

Diplomová práce pojednává o stabilizaci diskrétního logistického modelu pomocí několika řídících metod. Je zde provedena především stabilizace rovnováh, 2-periodických cyklů a 3-periodických cyklů. Ke stabilizaci systému je využito proporčního zpětně-vazebního řízení, zpětně-vazebního řízení s časovým zpožděním a řízení založeného na predikci. U každé metody je diskutovaná stabilizační množina pro řídící zesilovač spolu s oblastmi stability pro odpovídající kontrolovaná řešení. Všechny teoretické výsledky jsou ilustrovány grafickými interpretacemi v softwaru MATLAB. Podpůrné výpočty jsou provedeny pomocí softwaru Maple.

<p>To apply Industry 4.0 technologies and accelerate the modernization of continuous pharmaceutical manufacturing, digital twin (DT) and advanced process control (APC) strategies are indispensable. The DT serves as a virtual representation that mirrors the behavior of the physical process system, enabling real-time monitoring and predictive capabilities. Consequently, this facilitates the feasibility of real-time release testing (RTRT) and enhances drug product development and manufacturing efficiency by reducing the need for extensive sampling and testing. Moreover, APC strategies are required to address variations in raw material properties and process uncertainties while ensuring that desired critical quality attributes (CQAs) of in-process materials and final products are maintained. When deviations from quality targets are detected, APC must provide optimal real-time corrective actions, offering better control performance than the traditional open loop-control method. The progress in DT and APC is beneficial in shifting from the paradigm of Quality-by-Test (QbT) to that of Quality-by-Design (QbD) and Quality-by-Control (QbC), which emphasize the importance of process knowledge and real-time information to ensure product quality.</p> <p><br></p> <p>This study focuses on four key elements and their applications in a continuous dry granulation tableting process, including feeding, blending, roll compaction, ribbon milling and tableting unit operations. Firstly, the necessity of a digital infrastructure for data collection and integration is emphasized. An ISA-95-based hierarchical automation framework is implemented for continuous pharmaceutical manufacturing, with each level serving specific purposes related to production, sensing, process control, manufacturing operations, and business planning. Secondly, investigation of process analytical technology (PAT) tools for real-time measurements is highlighted as a prerequisite for effective real-time process management. For instance, the measurement of mass flow rate, a critical process parameter (CPP) in continuous manufacturing, was previously limited to loss-in-weight (LIW) feeders. To overcome this limitation, a novel capacitance-based mass flow sensor, the ECVT sensor, has been integrated into the continuous direct compaction process to capture real-time powder flow rates downstream of the LIW feeders. Additionally, the use of near-infrared (NIR)-based sensor for real-time measurement of ribbon solid fraction in dry granulation processes is explored. Proper spectra selection and pre-processing techniques are employed to transform the spectra into useful real-time information. Thirdly, the development of quantitative models that establish a link between CPPs and CQAs is addressed, enabling effective product design and process control. Mechanistic models and hybrid models are employed to describe the continuous direct compaction (DC) and dry granulation (DG) processes. Finally, applying APC strategies becomes feasible with the aid of real-time measurements and model predictions. Real-time optimization techniques are used to combine measurements and model predictions to infer unmeasured states or mitigate the impact of measurement noise. In this work, the moving horizon estimation-based nonlinear model predictive control (MHE-NMPC) framework is utilized. It leverages the capabilities of MHE for parameter updates and state estimation to enable adaptive models using data from the past time window. Simultaneously, NMPC ensures satisfactory setpoint tracking and disturbance rejection by minimizing the error between the model predictions and setpoint in the future time window. The MHE-NMPC framework has been implemented in the tableting process and demonstrated satisfactory control performance even when plant model mismatch exists. In addition, the application of MHE enables the sensor fusion framework, where at-line measurements and online measurements can be integrated if the past time window length is sufficient. The sensor fusion framework proves to be beneficial in extending the at-line measurement application from just validation to real-time decision-making.</p>