Prediction of Humidification Power in Textile-Spinning Mills Using Functional and Neural Networks

The power required for the supply, pumping, and exhaust system of the humidification plant of a spinning department of a textile mill depends on many factors. The most important factors are power of motor driving machinery, lighting and heating load, number of people inside, temperature gradient, and relative humidity. The usual procedure is to train the back-propagation neural network (BPN) with the available data and, once it is trained, BPN will be used for inferring. Some other investigators have used different neural network architectures. In this article, we give a general methodology to build and work with functional network (FN), an alternative to neural network paradigm. In this architecture neural functions, instead of weights, are learned. In addition to data, domain knowledge can also be incorporated. It is shown by means of an example that this functional network architecture is successfully applied to predict power required for the humidification plants.     

A note on plotting curves

In this note it is pointed out that sometimes there may be difficulties in plotting a curve using the well known interpolation techniques, and in such cases, “isoparametric style of” interpolation may be used for better accuracy.     

Removal of organics in water using hydrogen peroxide in presence of ultraviolet light

The optimum conditions for the removal of dissolved organic impurities from water using hydrogen peroxide (50%) followed by ultraviolet irradiation were investigated. The photochemically initiated hydroxyl radical (OH) oxidation reduced the total organic carbon (TOC) content of distilled water samples by about 88% and of tap water by 98%. Extraction with hexane of equal volumes of water samples before and after H₂O₂/u.v. treatment followed by gas chromatographic analysis of the concentrated extracts indicated that about 12% of the electron-capturing, residual organics remained after this treatment. These results support the conclusion drawn from total organic carbon analysis that this simple method yields water nearly free of organic impurities.     

Purification and kinetic characterization of polyphenol oxidase from Barbados cherry (Malpighia glabra L.)

Polyphenol oxidase (PPO) of Barbados cherry was extracted and purified through ammonium sulfate precipitation, gel filtration, and affinity chromatography. The purification factor for PPO was 60% with 8.3% yield. The enzyme was characterized for thermal stability, pH and kinetic parameters. The molecular mass of PPO was approximately the sum of 52 and 38 kDa estimated by SDS–PAGE. The purity was checked by native PAGE, showing a single prominent band. The optimum pH was 7.2. The enzyme had a temperature optimum at 40 °C and was relatively stable at 60 °C, with 55% loss of activity. Sodium diethyl dithiocarbamate (SDDC), l-cysteine and ascorbate significantly inhibited PPO activity. 4-Methyl catechol and catechol were found to be efficient diphenolic substrates for cherry PPO, considering the _Vmax/_Km$V_{\text{max}}/K_{\mathrm{m}}$ ratio. The data obtained in this study may help to understand cherry fruit browning.     

Water Pipeline Leak Measurement Using Wavelet Packet-based Adaptive ICA

Water Resources Management - The localization of the leaks is estimated using the time difference method in the past decade. Generalized and Basic Cross-correlation techniques are frequently...     

Dark fermentative biohydrogen production by Acinetobacter junii-AH4 utilizing various industry wastewaters

Hydrogen (H₂) is one of the most promising renewable energy sources, anaerobic bacterial H₂ fermentation is considered as one of the most environmentally sustainable alternatives to meet the potential fossil fuel demand. Bio-H₂ is the cleanest and most effective source of energy provided by the dark fermentation utilizing organic substrates and different wastewaters. In this study, the bio-H₂ production was achieved by using the bacteria Acinetobacter junii-AH4. Further, optimization was carried out at different pH (5.0–8.0) in the presence of wastewaters as substrates (Rice mill wastewater (RMWW), Food wastewater (FWW) and Sugar wastewater (SWW). In this way, the optimized experiments excelled with the maximum cumulative H₂ production of 566.44 ± 3.5 mL/L (100% FWW at pH 7.5) in the presence of Acinetobacter junii-AH4. To achieve this, a bioreactor (3 L) was employed for the effective production of H₂ and Acinetobacter junii-AH4 has shown the highest cumulative H₂ of 613.2 ± 3.0 mL/L, HPR of 8.5 ± 0.4 mL/L/h, HY of 1.8 ± 0.09 mol H₂/mol glucose. Altogether, the present study showed a COD removal efficiency of 79.9 ± 3.5% by utilizing 100% food wastewater at pH 7.5. The modeled data established a batch fermentation system for sustainable H₂ production. This study has aided to achieve an ecofriendly approach using specific wastewaters for the production of bio-H₂.     

Cluster head selection for energy efficient and delay-less routing in wireless sensor network

Wireless sensor network (WSN) is comprised of tiny, cheap and power-efficient sensor nodes which effectively transmit data to the base station. The main challenge of WSN is the distance, energy and time delay. The power resource of the sensor node is a non-rechargeable battery. Here the greater the distance between the nodes, higher the energy consumption. For having the effective transmission of data with less energy, the cluster-head approach is used. It is well known that the time delay is directly proportional to the distance between the nodes and the base station. The cluster head is selected in such a way that it is spatially closer enough to the base station as well as the sensor nodes. So, the time delay can be substantially reduced. This, in turn, the transmission speed of the data packets can be increased. Firefly algorithm is developed for maximizing the energy efficiency of network and lifetime of nodes by selecting the cluster head optimally. In this paper firefly with cyclic randomization is proposed for selecting the best cluster head. The network performance is increased in this method when compared to the other conventional algorithms.