Utilization of several plant proteins by gibel carp (Carassius auratus gibelio)

Six isonitrogenous (gross protein content 35%) and isoenergetic (gross energy content 17 kJ g(-1)) diets were formulated to investigate the effects of inclusion of plant proteins on the gibel carp (Carassius auratus gibelio L.). The plant proteins tested were: soybean cake (SBC), potato protein concentrate (PPC), peanut cake (PNC), cottonseed cake (CSC) and rapeseed cake (RSC). Fish meal (FM) was used as control. In each diet, 27% of the protein was supplied by fish meal, and the rest supplied by the plant protein tested. Each diet was fed to three groups of gibel carp for 8 weeks in a recirculation system. Specific growth rate (SGR) in fish fed the control diet was significantly higher than those in the other groups, and SGR in fish fed the PPC was significantly lower than in fish fed other plant proteins. There was no significant difference in SGR among the other groups. Feeding rates were ranked in the order: RSC > CSC > FM > PNC > SBC > PPC. Conversion efficiency was highest in groups fed FM, SBC and PNC, followed by groups fed CSC and RSC, and was lowest in the group fed PPC. The fish fed PPC showed lower protein retention than those fed FM and SBC. FM showed highest energy retention while PPC showed lowest, There was no significant relationship between SGR and intake of digestible protein (g g(-1) day(-1)), digestible lysine (g g(-1) day(-1)), digestible methionine (g g(-1) day(-1)) or digestible total essential amino acids (g g(-1) day(-1)), suggesting that the differences in SGR could not alone account for any of these variables.

Determining of the delay time for a heating ventilating and air-conditioning plant using two weighted neural network approach

This paper presents an two weighted neural network approach to determine the delay time for a heating, ventilating and air-conditioning (HVAC) plan to respond to control actions. The two weighted neural network is a fully connected four-layer network. An acceleration technique was used to improve the General Delta Rule for the learning process. Experimental data for heating and cooling modes were used with both the two weighted neural network and a traditional mathematical method to determine the delay time. The results show that two weighted neural networks can be used effectively determining the delay time for AVAC systems.

Determining of the delay time for a heating ventilating and air-conditioning plant using multi-weights neurons approach

This paper presents an multi weights neurons approach to determine the delay time for a Heating ventilating and air-conditioning (HVAC) plan to respond to control actions. The multi weights neurons is a fully connected four-layer network. An acceleration technique was used to improve the general delta rule for the learning process. Experimental data for heating and cooling modes were used with both the multi weights neurons and a traditional mathematical method to determine the delay time. The results show that multi weights neurons can be used effectively determining the delay time for HVAC systems.

Operational characteristics of a 1.2-MW biomass gasification and power generation plant

In this study, we analyzed the operational characteristics of a 1.2-MW rice husk gasification and power generation plant located in Changxing, Zhejiang province, China. The influences of gasification temperature, equivalence ratio (ER), feeding rate and rice husk water content on the gasification characteristics in a fluidized bed gasifier were investigated. The axial temperature profile in the dense phase of the gasifier showed that inadequate fluidization occurred inside the bed, and that the temperature was closely related to changes in ER and feeding rate. The bed temperature increased linearly with increasing ER when the feeding rate was kept constant, while a higher feeding rate corresponded to a lower bed temperature at fixed ER. The gas heating value decreased with increasing temperature, while the feeding rate had little effect. When the gasification temperature was 700-800C, the gas heating value ranged from 5450-6400kJ/Nm3. The water content of the rice husk had an obvious influence on the operation of the gasifier: increases in water content up to 15% resulted in increasing ER and gas yield, while water contents above 15% caused aberrant temperature fluctuations. The problems in this plant are discussed in the light of operational experience of MW-scale biomass gasification and power generation plants.

Design and Operation of A 5.5 MWe Biomass Integrated Gasification Combined Cycle Demonstration Plant

The design and operation of a 5.5 MWe biomass integrated gasification combined cycle (IGCC) demonstration plant, which is located in Xinghua, Jiangsu Province of China, are introduced. It is the largest complete biomass gasification power plant that uses rice husk and other agricultural wastes as fuel in Asia. It mainly consists of a 20 MWt atmospheric circulating fluidized-bed gasifier, a gas-purifying system, 10 sets of 450 kW(e) gas engines, a waste heat boiler, a 1.5 MWe steam turbine, a wastewater treatment system, etc. The demonstration plant has been operating since the end of 2005, and its overall efficiency reaches 26-28%. Its capital cost is less than 1200 USD/kW, and its running cost is about 0.079 USD/kWh based on the biomass price of 35.7 USD/ton. There is a 20% increment on capital cost and 35% decrease on the fuel consumption compared to that of a 1 MW system without a combined cycle. Because only part of the project has been performed, many of the tests still remain and, accordingly, must be reported at a later opportunity.