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The Solar Chimney Power Plant (SCPP) utilises a two-step procedure to transform solar energy into electricity. First, it uses a solar collector to turn sunlight into thermal energy. Then, this thermal energy is transformed into kinetic energy as it raises a chimney and finally into electrical energy via a wind turbine and generator. A numerical simulation of a prototype in Manzanares, Spain, was conducted using a 2D axi-symmetric model and computational fluid dynamics with an RNG k-turbulence model. The simulation also involved solving the radiative transfer equation with a two-band discrete ordinate radiation model. This study aims to evaluate the effect of vegetation beneath the collector roof on a solar chimney power plant’s performance. Our research compared various designs of these power plants, both with and without vegetation. Three configurations were examined in this study: a standard power plant, a power plant with a secondary collector roof, and a power plant with both secondary and tertiary collector roofs. According to our findings, the system with secondary and tertiary collector roofs demonstrated the highest electricity generation capacity, yielding an annual output ranging from 34 to 80 kW. The findings indicate that adding vegetation into a solar chimney power plant is feasible but will most likely reduce the plant’s energy generation.

Background: We evaluated a new, automated multicapillary zone electrophoresis (CE) instrument (Capillarys®, 4.51 software version; Sebia) for human serum protein analysis. Methods: With the Capillarys β1-β2+® reagent set, proteins were separated at 7 kV for 4 min in 15.5 cm × 25 μm fused-silica capillaries (n = 8) at 35.5 °C in a pH 10 buffer with online detection at 200 nm. Serum samples with different electrophoretic patterns (n = 265) or potential interference (n = 69) were analyzed and compared with agarose gel electrophoresis (AGE; Hydrasys®-Hyrys®, Hydragel protein(e) 15/30® reagent set; Sebia). Results: CVs were <3.5% for albumin, <11% for α1-globulin, <4.1% for α2-globulin, <7.4% for β-globulin, and <5.8% for γ-globulin (3 control levels); measured throughput was 60 samples/h. In patients without paraprotein (n = 116), the median differences between CE and AGE were −5.4 g/L for albumin, 4.0 g/L for α1-globulin, 0.7 g/L for α2-globulin, 0.6 g/L for β-globulin (P <0.001 for all fractions), and −0.1 g/L for γ-globulin (not significant). More samples had at least one γ-migrating peak detected by CE (n = 135 vs 130; paraprotein detection limit, ∼0.5–0.7 g/L), but fewer were quantified (n = 84 vs 91) because of γ- to β-migration shifts. There was a 1.2 g/L median difference between CE and AGE for γ-migrating paraprotein quantification (n = 69; P <0.001). Several ultraviolet-absorbing substances (lipid emulsion, hemoglobin) or molecules (contrast agent, gelatin-based plasma substitute) induced CE artifacts. Conclusions:The Capillarys instrument is a reliable CE system for serum protein analysis, combining advantages of full automation (ease of use, bar-code identification, computer-assisted correction of α1-globulins) with high analytical performances and throughput.

Numerical models are useful tools to evaluate problems and design remedial measures relative to groundwater seepage. They provide information for decision-making and guidance for collecting new data. Most users do not know in detail how the numerical code works. However, they must be sure that it gives reliable predictions for the problem under study. The major questions relative to the computer calculations are as follows: Can the results of the code be trusted? Under which conditions and to what extent are its predictions uncertain? This paper describes an approach that can be followed by model users to evaluate the results of a groundwater numerical code. This approach is relatively general, although each code is unique and may require specific controls. It begins with simple problems and progressively moves towards more complex problems: from steady-state to unsteady-state conditions, from one- to three-dimensional problems, and from saturated to saturated–unsaturated conditions. This approach is illustrated with a commercial code that passed the successive tests.Key words: groundwater, numerical code, quality control, saturated and unsaturated seepage.