Influence of static mixer on the cross-flow microfiltration of yeast suspensions

This work studies the influence of operating factors on the microfiltration of baker's yeast (Saccharomyces cerevisiae) in presence of static mixer as turbulence promoter. Microfiltration is typically used to remove particles in range 0.1-10 pm from a suspension. It is a pressure-driven process widely used in concentrating, purifying or separating suspended particles and macromolecules from solution. During cross-flow microfiltration process permeate flux decreases with time as the retained particles are accumulated on and within membrane surface area where they create additional resistance to permeate flow. External fouling of the membrane is the result of cell, cell fragments and rejected particles accumulation on the top of the membrane surface in the course of cake formation, while deposition of the macromolecules and small particles inside of the internal porous membrane structure results in internal fouling, which is often irreversible contrary to usually reversible external fouling. Experimental work was done with three membranes (A, B and C) with pore size 200, 450 and 800 nm. The results of microfiltration experiments for each membrane were estimated by analyzing the permeate flux without static mixer; permeate flux with static mixer; permeate flux improvement; reduction of specific energy consumption. Experimental results suggest that even though membrane A has smallest pore size of 200 nm it has the best performance considering steady state flux and reduction of specific energy consumption

What\u27s a Just War Theorist?

The article provides an account of the unlikely revival of the medieval Just War Theory, due in large part to the efforts of Michael Walzer. Its purpose is to address the question: What is a just war theorist? By exploring contrasts between scholarly activity and forms of international activism, the paper argues that just war theorists appear to be just war criminals, both on the count of aiding and abetting aggression and on the count of inciting troops to commit war crimes

Artemisinin Story from the Balkans

The isolation is reported of artemisinin (qinghaosu), a drug remarkably effective against malaria, from the aerial parts of Artemisia annua L. (sweet wormwood) at the Department of Chemistry, University of Belgrade (now Faculty of Chemistry), Serbia by the end of 1970, almost two years before the isolation of the same compound in China

Proizvodnja bioetanola iz ekstrahiranog soka kao međuproizvoda prerade šećerne repe: primjena metode odzivne površine

Response surface methodology (RSM) was used for selecting optimal fermentation time and initial sugar mass fraction in cultivation media based on raw juice from sugar beet in order to produce ethanol. Optimal fermentation time and initial sugar mass fraction for ethanol production in batch fermentation by free Saccharomyces cerevisiae cells under anaerobic conditions at the temperature of 30 °C and agitation rate of 200 rpm were estimated to be 38 h and 12.30 % by mass, respectively. For selecting optimal conditions for industrial application, further techno-economic analysis should be performed by using the obtained mathematical representation of the process (second degree polynomial model). The overall fermentation productivity of five different types of yeast was examined and there is no significant statistical difference between them.Metodom odzivne površine optimirano je vrijeme fermentacije i maseni udjel šećera u hranjivoj podlozi za proizvodnju etanola u kojoj je glavni sastojak sok ekstrahiran iz šećerne repe. Optimalno je vrijeme fermentacije 38 h i početni maseni udjel šećera od 12,30 % u proizvodnji etanola šaržnim uzgojem slobodnih stanica Saccharomyces cerevisiae u anaerobnim uvjetima pri temperaturi od 30 ºC i brzini miješanja od 200 o/min. Da bi se odredili optimalni uvjeti fermentacije u industrijskom mjerilu, potrebno je provesti daljnju tehnološko-ekonomsku analizu primjenom dobivenog matematičkog modela (polinom drugoga reda). Ispitana je produktivnost pet različitih tipova kvasaca i među njima nije utvrđena statistički značajna razlika

Application of metaheuristic optimization algorithms for image registration in mobile robot visual control

Visual Servoing (VS) of a mobile robot requires advanced digital image processing, and one of the techniques especially fitting for this complex task is Image Registration (IR). In general, IR involves the geometrical alignment of images, and it can be viewed as an optimization problem. Therefore, we propose Metaheuristic Optimization Algorithms (MOA) for IR in VS of a mobile robot. The comprehensive comparison study of three state-of-the-art MOA, namely the Slime Mould Algorithm (SMA), Harris Hawks Optimizer (HHO), and Whale Optimization Algorithm (WOA) is presented. The previously mentioned MOA used for IR are evaluated on 12 pairs of stereo images obtained by a mobile robot stereo vision system in a laboratory model of a manufacturing environment. The MATLAB software package is used for the implementation of the considered optimization algorithms. Acquired experimental results show that SMA outperforms HHO and WOA, while all three algorithms perform satisfactory alignment of images captured from various mobile robot poses

Application of metaheuristic optimization algorithms for image registration in mobile robot visual control

Visual Servoing (VS) of a mobile robot requires advanced digital image processing, and one of the techniques especially fitting for this complex task is Image Registration (IR). In general, IR involves the geometrical alignment of images, and it can be viewed as an optimization problem. Therefore, we propose Metaheuristic Optimization Algorithms (MOA) for IR in VS of a mobile robot. The comprehensive comparison study of three state-of-the-art MOA, namely the Slime Mould Algorithm (SMA), Harris Hawks Optimizer (HHO), and Whale Optimization Algorithm (WOA) is presented. The previously mentioned MOA used for IR are evaluated on 12 pairs of stereo images obtained by a mobile robot stereo vision system in a laboratory model of a manufacturing environment. The MATLAB software package is used for the implementation of the considered optimization algorithms. Acquired experimental results show that SMA outperforms HHO and WOA, while all three algorithms perform satisfactory alignment of images captured from various mobile robot poses

Methods for visual servoing of robotic systems: A state of the art survey

U ovom preglednom radu su prikazane metode vizuelnog upravljanja robotskih sistema, sa primarnim fokusom na mobilne robote sa diferencijalnim pogonom. Analizirane su standardne metode vizuelnog upravljanja bazirane na (i) greškama u parametrima slike (engl. Image-Based Visual Servoing - IBVS) i (ii) izdvojenim karakteristikama sa slike neophodnim za estimaciju položaja izabranog objekta (engl. Position-Based Visual Servoing - PBVS) i poređene sa novom metodom direktnog vizuelnog upravljanja (engl. Direct Visual Servoing - DVS). U poređenju sa IBVS i PBVS metodama, DVS metod se odlikuje višom tačnošću, ali i manjim domenom konvergencije. Zbog ovog razloga je DVS metod upravljanja pogodan za integraciju u hibridne sisteme vizuelnog upravljanja. Takođe, predstavljeni su radovi koji unapređuju sistem vizuelnog upravljanja korišćenjem stereo sistema (sistem sa dve kamere). Stereo sistem, u poređenju sa alternativnim metodama, omogućava tačniju ocenu dubine karakterističnih objekata sa slike, koja je neophodna za zadatke vizuelnog upravljanja. Predmet analize su i radovi koji integrišu tehnike veštačke inteligencije u sistem vizuelnog upravljanja. Ovim tehnikama sistemi vizuelnog upravljanja dobijaju mogućnost da uče, čime se njihov domen primene znatno proširuje. Na kraju, napominje se i mogućnost integracije vizuelne odometrije u sisteme vizuelnog upravljanja, što prouzrokuje povećanje robusnosti čitavog robotskog sistema.This paper surveys the methods used for visual servoing of robotic systems, where the main focus is on mobile robot systems. The three main areas of research include the Direct Visual Servoing, stereo vision systems, and artificial intelligence in visual servoing. The standard methods such as Image-Based Visual Servoing (IBVS) and Position-Based Visual Servoing (PBVS) are analyzed and compared with the new method named Direct Visual Servoing (DVS). DVS methods have better accuracy, compared to IBVS and PBVS, but have limited convergence area. Because of their high accuracy, DVS methods are suitable for integration into hybrid systems. Furthermore, the use of the stereo systems for visual servoing is comprehensively analyzed. The main contribution of the stereo system is the accurate depth estimation, which is critical for many visual servoing tasks. The use of artificial intelligence (AI) in visual servoing purposes has also gained popularity over the years. AI techniques give visual servoing controllers the ability to learn by using predefined examples or empirical knowledge. The learning ability is crucial for the implementation of robotic systems in a real-world dynamic manufacturing environment. Also, we analyzed the use of visual odometry in combination with a visual servoing controller for creating more robust and reliable positioning system

Mobile robot decision-making system based on deep machine learning

One of the major aspects of Industry 4.0 is enabling the manufacturing entities to operate in the dynamical systems autonomously. Therefore, to be autonomous, manufacturing entities need to have sensors to perceive their environment and utilize that information to make decisions regarding their actions. Having that in mind, in this paper, the authors propose a mobile robot decision-making system based on the integration of visual data and mobile robot pose. Mobile robot pose (current position and orientation) is integrated with two images gathered by two cameras and utilized to predict the possibility of gripping the part to be manufactured. A decision-making system is created by utilizing the deep learning model Resnet18 with an additional input for the mobile robot pose. The model is trained end-to-end and experimental evaluation is performed by using the mobile robot RACIO (Robot with Artificial Intelligence based COgnition)