Model Predictive Control of Vehicle Charging Stations in Grid-Connected Microgrids:An Implementation Study

The transition to renewable energy sources, particularly sources like wind and solar induces a dependency on weather in the supply side of electrical grids. At the same time, the move to electric mobility with uncontrolled charging induces extra peak loads on these grids. These developments can cause grid congestion or an imbalance between the renewable power supply and the demand. Locally balancing the power supply and demand in grid-connected microgrids can alleviate such issues on the main grid. This paper presents a model based control strategy to address the challenge of locally balancing the power supply and demand in a grid-connected microgrid to avoid reaching the threshold rated power output set for large buildings. The microgrid under consideration consists of photovoltaic power sources and a large fleet of electric vehicle chargers (>150). A model predictive controller is developed that treats the daily vehicle charging as a batch process. Given vehicle charge objectives, the controller utilizes vehicle charger occupancy and photovoltaic power generation forecasting services to distribute power optimally over a fixed period of time. The optimization problem is formulated as a quadratic programming problem and is implemented utilizing open-source Python libraries. The controller was integrated into the control system of a microgrid situated at a corporate office in the Netherlands. The control system oversaw the operation of 174 vehicle chargers. The effectiveness of the model predictive control technology was demonstrated over a three-week period and led to an average daily grid peak power reduction of 59%

Effects of hyperbaric oxygen treatment on implant osseointegration in experimental diabetes mellitus

PubMedID: 29995150CONCLUSION: Histomorphometry findings suggest that HBO has positive effect on implant osseointegration in the early healing period in diabetic rabbits. However, implant stability is not affected by HBO treatment.RESULTS: The Bone Implant Contact (BIC) values were significantly higher in the HBO group than in the control group at the 4th week. There was no difference in the BIC values between the groups at the 8th week. There was no significant difference in the RFA scores between the groups both at the 4th and 8th weeks after the operation.OBJECTIVE: To evaluate whether hyperbaric oxygen (HBO) treatment has a favorable effect on implant osseointegration in diabetic rabbits.MATERIAL AND METHODS: An experimental diabetes model was induced in 32 New Zealand rabbits through IV injection of alloxan. After the state of diabetes had been confirmed, one dental implant was placed in the metaphysical region of each animal's tibia. After the implants' placements, the animals were divided into two groups. Half of the animals underwent HBO treatment, while the other group did not receive HBO treatment and served as the control group. The animals were euthanized at the 4th and 8th weeks. The osseointegration of the implants were compared by histomorphometry and resonance frequency analysis (RFA)

Assessment of a bio-molecular sensor in the operation of adsorption processes-A model based approach

Adoption of digitalisation and Industry 4.0 concepts is gaining pace in food manufacturing and bio-based processes [1]. An essential aspect is the introduction of real time sensors in order to improve process monitoring, decision making and process control. To this end, we assess the use of a novel technology for the real-time sensing of biomolecular substances [2, 3] for the monitoring and control of adsorption processes in the food industry.In recent years, the mechanistic modeling of adsorption processes has got attention since they can be used in optimization, scale-up etc. However, the development of bottom-up models takes a long time and requires estimation of several process parameters from experimental data. Instead, we make use of simple mathematical models (double exponential or logistic function, [4]) to describe adsorption breakthrough curves, which express the concentration of an adsorbate in the fluid phase at the outlet of an adsorption column as a function of time [5]. Our work shows that such a simple model can be used to study how a biosensor placed at the inlet of a column can be used to steer decisions when to stop the adsorptions process. Furthermore, the model can also be used to design a feedback -feedforward control strategy to achieve a constant concentration at the outlet of a series of adsorption columns which was studied for an extraction process of an anti-nutritional factor. The results of our study shows that the integration of a real-time sensing technology could help to track, analyze and optimize production processes in the food industry.This work was partly funded by The Netherlands Topsectors Agri&Food, HTSM, and Chemistry under contract number LWV20.117

GPR30 gene polymorphisms are associated with gynecomastia risk in adolescents

Aim: The G protein-coupled receptor, GPR30, which is a third estrogen receptor, has been shown to mediate estrogenic effects on the essential features of human breast cancer cells. The aim of this study was to evaluate the association between GPR30 single nucleotide polymorphisms and gynecomastia in males. Methods: This study included 109 male adolescents with gynecomastia and 104 controls. Follicle stimulating hormone, luteinizing hormone, total testosterone, estradiol (E2), dehydroepiandrosterone sulfate (DHEAS), and prolactin levels were measured. DNA was extracted from whole blood using a GeneJET Genomic DNA purification kit. The genotypes of the GPR30 gene (rs3808350, rs3808351 and rs11544331) were studied using a tetra-primer ARMS (amplification refractory mutation system) PCR approach. Results: The median E2 (11.80 vs. 16.86 IU/l, p < 0.001) and DHEAS levels (116.8 vs. 146.5 µg/dl, p = 0.044) were higher in the gynecomastia group. The G allele of rs3808350 and the A allele of rs3808351 were frequently observed in patients with gynecomastia. Gynecomastia was more common in patients with the GG genotype of rs3808350 and in patients with the AA genotype of rs3808351. Conclusions: Our results suggest that increased E2 levels, the G allele of rs3808350 and the A allele of rs3808351 might explain why certain adolescents are affected by gynecomastia. © 2014 S. Karger AG, Basel

Advanced autonomous model-based operation of industrial process systems (Autoprofit): Technological developments and future perspectives

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