Model Predictive Control Technique of Multilevel Inverter for PV Applications

Renewable energy sources, such as solar, wind, hydro, and biofuels, continue to gain popularity as alternatives to the conventional generation system. The main unit in the renewable energy system is the power conditioning system (PCS). It is highly desirable to obtain higher efficiency, lower component cost, and high reliability for the PCS to decrease the levelized cost of energy. This suggests a need for new inverter configurations and controls optimization, which can achieve the aforementioned needs. To achieve these goals, this dissertation presents a modified multilevel inverter topology for grid-tied photovoltaic (PV) system to achieve a lower cost and higher efficiency comparing with the existing system. In addition, this dissertation will also focus on model predictive control (MPC) which controls the modified multilevel topology to regulate the injected power to the grid. A major requirement for the PCS is harvesting the maximum power from the PV. By incorporating MPC, the performance of the maximum power point tracking (MPPT) algorithm to accurately extract the maximum power is improved for multilevel DC-DC converter. Finally, this control technique is developed for the quasi-z-source inverter (qZSI) to accurately control the DC link voltage, input current, and produce a high quality grid injected current waveform compared with the conventional techniques. This dissertation presents a modified symmetrical and asymmetrical multilevel DC-link inverter (MLDCLI) topology with less power switches and gate drivers. In addition, the MPC technique is used to drive the modified and grid connected MLDCLI. The performance of the proposed topology with finite control set model predictive control (FCS-MPC) is verified by simulation and experimentally. Moreover, this dissertation introduces predictive control to achieve maximum power point for grid-tied PV system to quicken the response by predicting the error before the switching signal is applied to the converter. Using the modified technique ensures the iii system operates at maximum power point which is more economical. Thus, the proposed MPPT technique can extract more energy compared to the conventional MPPT techniques from the same amount of installed solar panel. In further detail, this dissertation proposes the FCS-MPC technique for the qZSI in PV system. In order to further improve the performance of the system, FCS-MPC with one step horizon prediction has been implemented and compared with the classical PI controller. The presented work shows the proposed control techniques outperform the ones of the conventional linear controllers for the same application. Finally, a new method of the parallel processing is presented to reduce the time processing for the MPC

Model Predictive Control Technique of Multilevel Inverter for PV Applications

Renewable energy sources, such as solar, wind, hydro, and biofuels, continue to gain popularity as alternatives to the conventional generation system. The main unit in the renewable energy system is the power conditioning system (PCS). It is highly desirable to obtain higher efficiency, lower component cost, and high reliability for the PCS to decrease the levelized cost of energy. This suggests a need for new inverter configurations and controls optimization, which can achieve the aforementioned needs. To achieve these goals, this dissertation presents a modified multilevel inverter topology for grid-tied photovoltaic (PV) system to achieve a lower cost and higher efficiency comparing with the existing system. In addition, this dissertation will also focus on model predictive control (MPC) which controls the modified multilevel topology to regulate the injected power to the grid. A major requirement for the PCS is harvesting the maximum power from the PV. By incorporating MPC, the performance of the maximum power point tracking (MPPT) algorithm to accurately extract the maximum power is improved for multilevel DC-DC converter. Finally, this control technique is developed for the quasi-z-source inverter (qZSI) to accurately control the DC link voltage, input current, and produce a high quality grid injected current waveform compared with the conventional techniques. This dissertation presents a modified symmetrical and asymmetrical multilevel DC-link inverter (MLDCLI) topology with less power switches and gate drivers. In addition, the MPC technique is used to drive the modified and grid connected MLDCLI. The performance of the proposed topology with finite control set model predictive control (FCS-MPC) is verified by simulation and experimentally. Moreover, this dissertation introduces predictive control to achieve maximum power point for grid-tied PV system to quicken the response by predicting the error before the switching signal is applied to the converter. Using the modified technique ensures the iii system operates at maximum power point which is more economical. Thus, the proposed MPPT technique can extract more energy compared to the conventional MPPT techniques from the same amount of installed solar panel. In further detail, this dissertation proposes the FCS-MPC technique for the qZSI in PV system. In order to further improve the performance of the system, FCS-MPC with one step horizon prediction has been implemented and compared with the classical PI controller. The presented work shows the proposed control techniques outperform the ones of the conventional linear controllers for the same application. Finally, a new method of the parallel processing is presented to reduce the time processing for the MPC

Biochar as a Soil Amendment for Restraining Greenhouse Gases Emission and Improving Soil Carbon Sink: Current Situation and Ways Forward

The global exponential rise in greenhouse gas (GHG) emissions over the last few decades has triggered an urgent need to contextualize low-cost and evergreen technologies for restraining GHG production and enhancing soil carbon sink. GHGs can be mitigated via incorporating biochar into soil matrix to sequestrate the mineralized carbon in a stable form upon organic matter decomposition in soil. However, the efficiency of using biochar to offset GHG emissions from soil and terrestrial ecosystems is still debatable. Moreover, in the literature, biochar shows high functionality in restraining GHG emissions in short-term laboratory studies, but it shows minimal or negative impacts in field-scale experiments, leading to conflicting results. This paper synthesizes information on the ability of biochar to mitigate carbon dioxide (CO2), nitrous oxide (N2O), and methane (CH4) emissions from soil and organic biomass, with an emphasis on cropland soils. The feedstock type, pyrolysis temperature, and application rate factors showed significant effects on controlling the effectiveness of biochar in restraining GHG emissions. Our study demonstrates that biochar, taken as a whole, can be seen as a powerful and easy-to-use tool for halting the rising tide of greenhouse gas emissions. Nonetheless, future research should focus on (i) identifying other indirect factors related to soil physicochemical characters (such as soil pH/EH and CaCO3 contents) that may control the functionality of biochar, (ii) fabricating aged biochars with low carbon and nitrogen footprints, and (iii) functionalizing biologically activated biochars to suppress CO2, CH4, and N2O emissions. Overall, our paradoxical findings highlight the urgent need to functionalize modern biochars with a high capacity to abate GHG emissions via locking up their release from soil into the carbonaceous lattice of biochar

Non-differential interaction between isolates of Rhizoctonia solani and flax cultivars

The pathogenicity of 24 isolates of Rhizoctonia solani (7 isolates from anastomosis group AG-2 and 17 from AG-4) was evaluated on 10 flax cultivars under greenhouse conditions. Survival, plant height, and dry weight were used as criteria to evaluate pathogenicity. Analysis of variance (ANOVA) showed that the cultivar was a highly significant source of variation in all the tested parameters (P < 0.0002). Isolate was always a highly significant source of variation in all the tested parameters (P = 0.0000). Cultivar x isolate interaction was always a nonsignificant source of variation. The results of the ANOVA in the present study suggest that physiologic specialization did not occur within R. solani isolates pathogenic on flax. They also imply that resistance of the tested cultivars was only horizontal, and there were significant differences among cultivars in this type of resistance. Similarly, pathogenicity of the tested isolates was only aggressiveness, and the isolates significantly differed in this type of pathogenicity. A hierarchical cluster analysis was conducted in order to group the isolates according to disease variables measured on the tested cultivars. Cluster analysis divided the isolates into groups; however, grouping the isolates was not related to their geographic origin nor the AG.Sýkingareiginleikar 24 stofna af Rhizoctonia solani (7 stofnar af netjuðum sveppum AG-2 og 17 af AG-4) voru metnir á 10 kvæmum af hör í gróðurhúsi. Lifun, hæð plöntu og þurrefni voru notuð til að meta sýkingareiginleika. Fervikagreining (ANOVA) sýndi að kvæmi var mjög marktæk uppspretta breytileika í öllum þáttum sem skoðaðar voru (P < 0.0002). Stofn var alltaf mjög marktæk uppspretta breytileika í öllum þáttum sem skoðaðir voru (P = 0.0000). Víxlverkun á milli stofns og kvæmis var aldrei marktæk ástæða breytileika. Niðurstöður fervikagreiningarinnar benda til þess að engin lífeðlisfræðileg sérhæfing hafi átt sér stað í R. solani stofnun sem sýkja lín. Jafnframt benda niðurstöðurnar til þess að varnir kvæmanna sem prófaðir voru séu einungis byggðar á almennri mótstöðu og að það væri marktækur munur milli kvæma í þeirri gerð mótstöðu.. Jafnframt að sýkingareiginleikar stofnanna sem prófaðir voru séu eingöngu háðir sýkingarhæfni og að munur hafi verið á stofnunum hvað þetta varðar. Klasagreining skipti sveppastofnunum í hópa en þeir voru hvorki tengdir landfræðilegum uppruna né AG

Additional file 1 of The burden of persistent symptoms after COVID-19 (long COVID): a meta-analysis of controlled studies in children and adults

Additional file 1. Table S1: Supplementary preferred reporting items for systematic reviews and meta-analyses (PRISMA) checklist; Table S2: Full search strategy; Table S3: Checklist items for quality assessment of the included studies; Table S4: List of excluded studies; Table S5: Quality assessment of the included studies; Table S6: Pooled odds ratios for clinical signs and symptoms across all included studies stratified by patients’ age category regardless the hospitalization state; Fig. S1: Funnel plot of dyspnea in non-hospitalized COVID-19 patients relative to negative control; Fig. S2: Funnel plot of fatigue in non-hospitalized COVID-19 patients relative to negative control; Fig. S3: Funnel plot of brain and memory deficits in non-hospitalized COVID-19 patients relative to negative control

Synthesis, Physicochemical Characterization using a Facile Validated HPLC Quantitation Analysis Method of 4-Chloro-phenylcarbamoyl-methyl Ciprofloxacin and Its Biological Investigations

A novel derivative of ciprofloxacin (Cpx) was synthesized and characterized using various analytical techniques, including FT-IR spectroscopy, UV-Vis spectroscopy, TEM and SEM analysis, 1H NMR, 13C NMR, and HPLC analysis. The newly prepared Cpx derivative (Cpx-Drv) exhibited significantly enhanced antibacterial properties compared to Cpx itself. In particular, Cpx-Drv demonstrated a 51% increase in antibacterial activity against S. aureus and a 30% improvement against B. subtilis. It displayed potent inhibitory effects on topoisomerases II (DNA gyrase and topoisomerase IV) as potential molecular targets, with IC50 values of 6.754 and 1.913 µg/mL, respectively, in contrast to Cpx, which had IC50 values of 2.125 and 0.821 µg/mL, respectively. Docking studies further supported these findings, showing that Cpx-Drv exhibited stronger binding interactions with the gyrase enzyme (PDB ID: 2XCT) compared to the parent Cpx, with binding affinities of −10.3349 and −7.7506 kcal/mole, respectively