Simple model and control strategy of earth-to-air heat exchangers
The worldwide energy situation forces industrialized countries to reduce their energy consumption, especially in the building sector, which makes up a high proportion of the total consumption. Techniques such as earth-to-air heat exchangers must therefore be developed and managed to meet this challenge. Yet earth-to-air heat exchangers provide better performance when linked to efficient control strategies.
Genetic algorithms based optimization of artificial neural network architecture for buildings’ indoor discomfort and energy consumption prediction
Growing concerns about energy consumption reduction and comfort improvement inside buildings make it more and more necessary to be able to predict with fine precision building’s heating loads and indoor discomfort. This article proposes a method based on genetic algorithms (GAs) to optimize the architecture, training parameters and inputs of an artificial neural network (ANN). The ANN is doomed to predict energy consumption and indoor discomfort in future work on the development of an on-line method for control setting optimization. Simple and advanced controllers were used in this study: ON-OFF, PID and fuzzy controllers. Validation of the optimized ANN showed good prediction accuracy, as regression coefficients R2 for consumption and discomfort were respectively greater than 0.77 and 0.84 for the three tested controllers. Various prediction “distances” and ANN training data quantities were tested. Conclusion is that prediction at a 2-hour “distance” and a 3-day quantity of data are the best tested optimization conditions.
Parboiled rice effluent: A wastewater niche for microalgae and cyanobacteria with growth coupled to comprehensive remediation and phosphorus biofertilization
Abstract The potential of microalgae and cyanobacteria for bioremediation of wastewater by nutrient uptake combined with simultaneous biomass production is a well recognized perception of today's world. The present study illustrates the treatment of a highly polluted wastewater generated during parboiling of paddy in rice mill industries, widely operational in developing countries where rice is the staple food crop, with the help of microalgal and cyanobacterial isolates capable of growing at high rates in parboiled rice mill effluent (RME). This endeavor leads to comprehensive bioremediation of the said effluent and subsequent use of the harvested biomass as slow release phosphorus biofertilizers and the treated effluent for crop irrigation. The RME-acclimatized algal consortium demonstrated highest growth in terms of fresh weight and greatest remediation efficiency at the end of 36 days' treatment of RME, with 93.9% phosphorus and 100% ammonia-nitrogen removal, 98.7%, 91.6% and 93.5% reduction in biological oxygen demand, chemical oxygen demand and total dissolved solid, respectively, and an increment of 186 ± 0.3 mg L − 1 dissolved oxygen, bringing down the pollutants level well below the discharge limits suggested by Central Pollution Control Board, India. Additionally, microalgae in the consortium aggregated in clumps spontaneously in presence of the filaments of Phormidium sp. facilitating easy harvest. The RME-acclimatized algal consortium demonstrated highest accumulation of polyphosphate (poly-P) (0.76 ± 0.01% of dry weight) as well as highest release of phosphorus in non-sterile soil emphasizing the essential role of soil phosphorus solubilizing organisms to leach soluble phosphorus from the insoluble poly-P present in the biomass. The rice seedlings watered with treated RME also showed improved growth effect on shoot height and leaf width. The study results establish the suitability of RME as an excellent growth media for microalgae and cyanobacteria.