Abstract

This article illustrates the investigation of the photovoltaic generation system. The topology of the system is to design a number of PV modules based on control models of DC-DC boost converter and voltage source converter are associated to provide a certain rate of power for a household power demand. The testbed of the system was approached individually. simulation results show a significant improvement in the system in terms of reliability and capability under different irradiances and temperatures.

The topology of the system was designed separately on MATLAB Simulink software. The sizing and modeling rate power of PV panels are achieved under two different irradiances and temperature was assumed constant. A boost converter is designed to be interfaced between PV panels and voltage source converter (VSC) to convert low DC voltage to much higher output DC voltage which represents the input voltage of the voltage source converter (VSC). This conversion of power is achieved by a number of control techniques such as a pulse width modulation (PWM) applied to the semiconductor switch and also the reference signal controlled by PID controller to maintain the output voltage constant. The purpose of the voltage source converter is to convert DC voltage to AC output voltage to be the last conversion stage to achieve the required power for householder demand. The work that has been achieved so far is as follows:

• A study has been carried out in order to understand the application and the technical state of PV modules, dc-dc boost converter, and voltage source converter.
• Calculating the power rate of PV module and size based on load demand assumed at 5 KW.
• Rating power of PV panel has been calculated based on datasheet provided from the manufacturer at 120Vdc, 60A, and 7.2KW.
• DC-DC boost converter has been Designed and evaluated to convert the output of the PV which is 120Vdc to 311Vdc output voltage as a peak value of the output of voltage source converter which would be 220Vac.
• Voltage source converter (VSC) has been designed and investigated based on the output of the boost converter.
• Simulate each stage individually (test check) by using MATLAB/Simulink software.

Over the last decade, biocides have received increasing attention due to their widespread use, their transfer to aquatic ecosystems and their negative effect on aquatic organisms. Alkyl benzyl dimethyl ammonium chlorides are applied as bactericides and disinfectants in sanitary products and antistatic agents in the formula of laundry conditioners. The aim of this study was to provide a sensitive and robust HPLC-DAD method for detection of three biocides (dodecyl- (C12-), tetradecyl- (C14-), and hexadecyl- (C16-) benzyl dimethylammonium chloride) in wastewater samples. The analytes separation was achieved using an Acclaim Surfactant Plus (3 µm, 150 mm x 3 mm) chromatographic column, maintained at 30°C. The isocratic mode elution using an binary phase of ammonium acetate 0,2 M (A) : acetonitrile (B) as mobile phase (50:50, v/v) at a flow rate of 0.5 ml/min, allowed a run time of only 5 minutes. The linearity, accuracy and intermediate precision were validated. The HPLC-DAD method provides good linearity, with correlation coefficients from 0.9992 to 0.9997 in the concentration range from 1 to 100 mg/L. Very good precision values were obtained, with RSD% ranged from 1.37-2.27% for intra-day measurements and between 6.14 and 6.65% for inter-day measurements. The target biocides were isolated from wastewater samples through Solid Phase Extraction (SPE) procedure, using polymeric Strata-X Cartridges and acetonitrile and acetic acid (90%/10%) as elution solvent mixture. Recoveries (up to 86%) made possible the quantification biocides at very low levels, the limits of quantification (LOQs) being in the range of 4.5 and 7.6 µg/L. The method was successfully applied to wastewater samples, obtaining concentration values varying from few µg/L to few mg/L.

A core element for Industry 4.0 is the digital twin: a virtual model of a product or process created with data collected by sensors that enables simulations or real-time analyses of the status of production. The use of digital twins seems beneficial in food processing for various reasons. To ensure the supply of food, production processes must allow a high flexibility and adaptivity. Furthermore, product quality is influenced by different quality levels of input materials. Especially in case of seasonal fluctuations of this raw material quality, an adjustment of parameters in the production process is essential. Introducing new products that are related to existing ones is also a challenge in food manufacturing. These introduction processes could be simplified by a digital twin of already existing products.

However, digital twins of food products have additional specific requirements compared to digital twins of material goods. Due to the variability of raw materials, these cannot be based only on the processing steps, but must also take into account the chemical, physical, or (micro)biological properties of food.

We have the vision to create a digital food twin that can be used to track the current state of production at any time. While Industry 4.0 approaches often focus on the analysis of machine data, this project aims at a product-related data analysis (e.g., the effects of pressure exerted by machines). With the help of machine learning (ML) and artificial intelligence methods, the digital twin will be generated from production data and other data sources (e.g., scientific models, process data, or raw material data) to ensure the traceability of the current production and the food status, but also to enable the simulation of the variability of the food in the processing process.

Composting has demonstrated to be an effective and sustainable technology to treat a wide variety of organic wastes. The process is based on the microbial decomposition of organic matter under aerobic conditions to obtain compost: an organic amendment that can be safely used in agriculture and other applications because of its stable and mature characteristics. Among the composted wastes, the organic fraction of municipal waste has received special attention. One particular aspect of composting is the variety of technological options that can be used for composting, from full-scale plants to individual composters. In contrast to big facilities, an increasing number of initiatives using home or community composting have appeared in different parts of the world. Initially, these experiences were explained as hobbies and low scientific information could be obtained from them. Today, we have a scientifically based information about home and community composting, in different aspects such as the performance of the process, the quality of home compost or even its environmental impact and Life Cycle Assessment.

In this sense, the interest in composting at home or community scale is exponentially growing in recent years, as it permits the self-management of organic wastes and obtaining a compost that can be used by the own producer. However, some questions about the quality of the obtained compost, the environmental impact of home composting and the feasibility of vermicomposting are stage in an early stage of this development and knowledge. In this review, the main points related to home and community composting are analysed in detail according to the scientific current knowledge and highlighting their advantages and possible drawbacks.

Combustion and explosion of combustible mixtures are a major hazard that can occur anywhere from industry to energy use in households and therefore, protective measures must be taken to limit these undesirable events. This study pays attention to the laminar burning velocity, an important parameter involved in the combustion process. The experimental laminar burning velocities of stoichiometric methane-nitrous oxide mixtures in the presence of diluents (50 vol% inerts: argon, helium and carbon dioxide) were calculated from pressure-time records obtained in a spherical vessel with central ignition, using a correlation based on the cubic law of pressure rise during the early stage of explosion. The nitrous oxide (N₂O) based mixtures are frequently used as propellants in propulsion systems and supersonic wind tunnels, due to the nontoxicity, high saturation pressure and the exothermic property during decomposition. However, N₂O is an oxidizer that can cause safety concerns in technical applications in which it is involved. The experimental data were compared with literature data on stoichiometric methane-nitrous oxide mixtures diluted with nitrogen and with the calculated laminar burning velocities obtained by numerical modelling of their premixed flames. The modelling was performed with Cosilab package, using GRI 3.0 mechanism, based on 53 chemical species and 325 elementary reactions. The influence of initial pressure (0.5 bar – 1.75 bar) of stoichiometric inert-diluted methane-nitrous oxide mixtures on laminar burning velocities, maximum flame temperature, heat release rate and peak concentrations of main reaction intermediates was investigated and discussed. Using the correlations of the laminar burning velocities with the initial pressure, the pressure exponent and overall reaction order of methane oxidation with nitrous oxide were determined. Obtaining a clear perspective on the laminar burning velocities of these flammable mixtures is of great importance for both assessing fire and explosion risks and guaranteeing safety in chemical and process industries.

The use of lithium-ion batteries allows to reliable and efficient storage of electricity.Commercial batteries use flammable liquid organic electrolytes, which have low thermal andelectrochemical stability. Replacing liquid electrolytes with solid ones will solve these problems.NASICON structured electrolytes, in particular LATP (Li1+yTi2-yAly(PO4)3) and LAGP (Li1+yGe2-3yAly(PO4) ), are among the most promising electrolytes for all-solid-state batteries. Partialreplacement of titanium ions by germanium ions can lead to materials that combine the highlithium-ion conductivity of LATP with the high chemical stability of LAGP. The aim of thiswork was to synthesize and study the ionic mobility of Li1+yTi2-x-yGexAly(PO4)3 (x = 0-2, y = 0-0.3) with the NASICON structure.Li1+yTi2-x-yGexAly(PO4)3 (x = 0-2, y = 0-0.3) electrolytes were synthesized by the solid-state method and investigated using X-ray diffraction and scanning electron microscopy,impedance spectroscopy, and NMR spectroscopy. The processes occurring during the solid-statesynthesis of Li1+yTi2-x-yGexAly(PO4)3 have been studied. An increase in conductivity from10-7 S/cm to 4.6.10-6 S/cm at 25°C was found when 10% titanium ions were replaced bygermanium. Additional introduction of aluminum results in increase in lithium conductivity up to1.4.10-4 S/cm (25 °C). Since grain boundaries are of decisive importance for the overall ionicconductivity of the NASICON-structured phosphates, the influence of precursor mechanicaltreatment on the microstructure and ionic conductivity of the prepared materials was studied.The use of mechanical treatment leads to a significant increase in grain size (reducing the grainboundaries and its resistance) and an increase in ionic conductivity (up to 6.4.10-4 S/cm at 25 °C).The obtained materials can be considered as promising solid electrolytes for all-solid-statelithium batteries with high safety and stability.

Owing to high thermal and chemical stability and good mechanical properties, polybenzimidazole (PBI) doped with phosphoric acid is a very promising material to be used as an electrolyte in the medium-temperature fuel cells. Their use at temperatures below ~160°C is impeded by the leaching of the free H₃PO₄ from the membrane. In order to overcome this problem one of the possible approaches is the incorporation of inorganic particles capable to stabilize H₃PO₄ in PBI matrix. Surface-modified particles can be more efficient for this purpose.

In this work we studied the properties of proton-conducting membranes based on PBI and silica particles surface-modified by propylphosphonic groups.

Composite membranes were obtained by casting of polymer solution containing tetraethoxysilane and modified silane ((2-diethylphosphatoethyl)triethoxysilane) with next hydrolysis by HCl. The mass concentration of the dopant was 5 or 10 wt %, and the mole fraction of functional groups on the oxide surface was varied in the range of 0–100 mol % by changing the composition of the precursor mixture. All films were treated by 75% H₃PO₄.

The resulting membranes have been characterized using transmission and scanning electron microscopy, IR spectroscopy, and impedance spectroscopy. Grafting of functional –PO₃H₂ groups onto the silica surface leads to a significant increase in the uptake of phosphoric acid by hybrid membranes, the content of which determines the conductivity of these materials. An increase in the number of –PO₃H₂ groups leads to both an increase in the degree of acid doping and ionic conductivity. The conductivity of the best samples obtained reaches 0.081 S/cm at 160°C. The introduction of acid groups on the dopant surface is a promising approach from the point of view of reducing the amount of phosphoric acid required to maintain a high proton transport rate.

Today metal-sulfur batteries are among the most promising rechargeable power sources. However, there are several problems that limit the use of metal-sulfur batteries. Among them low electrical conductivity of sulfur, significant change in the volume of the cathode (about 160%) during the discharge/charge process, migration of soluble polysulfides of alkali metal between electrodes should be mentioned. Optimizing of the composition and microstructure of the cathode material coupled with the cation selective separator can solve or minimize the above mentioned problems. This work is devoted to comparative study of the electrochemical properties of S/C composites with different carbon matrices such as carbon nanotubes (NanoTechCenter LTD, Russia), mesoporous carbon (NanoTechCenter LTD, Russia) as cathode materials for lithium- and sodium-sulfur batteries. Liquid electrolyte with polypropylene film or cation exchange Nafion membranes intercalated by polar aprotic solvents were used as electrolyte and separator.

This contribution investigates the performance of nature-inspired multi-objective optimization algorithms to reduce makespan and oven idle time of bakery manufacturing using a hybrid no-wait flow shop scheduling model. As an example, the production data from a bakery with 40 products is investigated. We use the Non-dominated sorting genetic algorithm (NSGA-II) and multi-objective particle swarm optimization (MOPSO) to determine the tradeoffs between the two objectives. The computational results reveal that the nature-inspired optimization algorithms provide solutions with a significant 8.7 % reduction in makespan. Nonetheless, the algorithms provide solutions with a longer oven idle time to achieve the single goal of makespan minimization. This consequently elevates energy waste and production expenditure. The current study shows that an alternative Pareto optimal solution significantly reduces oven idle time while losing a marginal amount of makespan. Furthermore, Pareto solution reduces oven idle time by 93 minutes by expanding the makespan by only 8 minutes. The proposed approach has the potential to be an influential tool for small and medium-sized bakeries seeking economic growth and, as a result, gain in market competition.

The combined action of Machine Learning and Control System Algorithm is proposed in this Renewable Energy System. The reason for proposing the Renewable Energy System, which is the clean energy source from the nature and it’s free of cost. Here the Renewable Energy system includes Solar PV System. Since this energy system has a higher scope of installation in most countries. For that, we propose a controller which achieves high performance and Guarantees Stability. In this proposed system the disturbance and Uncertain parameters are considered both internal and external parameters. To overcome this problem much Robust Control design is being already implemented in the Control Engineering Field to attain System Stability. Whereas this proposed method is a new approach to examining the System Stability by combining Machine Learning - Gaussian Process Regression (ML-GPR) with Robust H-infinity Controller. The major approach used in Machine Learning-GPR is to gather the data of the initial system and gradually decrease the Uncertainty, which results in improving the performance. Finally, ML-GPR learns a model with Uncertainty bounds. Now we combine a Control Framework (i.e., H-infinity Controller) that Guarantees Stability for this uncertain model. The design Environment used for the experimental verification is MATLAB/Simulink software. The Simulation Results confirmed the effectiveness of the newly proposed Control Strategy.