[Objective] The aim of the study was to establish the effective and accurate formulas for estimating the digestible energy （DE） values of plant protein supplement in pig. [Method] By difference method with different...[Objective] The aim of the study was to establish the effective and accurate formulas for estimating the digestible energy （DE） values of plant protein supplement in pig. [Method] By difference method with different amount of alternative feeds （20% -50%）, two4 x4 Latin- square-designed trials were taken on eight castrated male pigs [ Yorkshire x Landrace x Neijiang pig, initial body-weight： （46 ±2） kg ] to deter- mine the apparent digestible energy （ADE） of the eight kinds of plant protein supplement commonly used in China, that is, corn gluten meal （sol.）, soybean meal （ sol. ）, fababean, pea, rapeseed meal （ sol. ）, sesame meal （ sol. ）, rapeseed meal （ exp. ） and cotton seed meal （sol.）. [Resultl （1） Fiber was the most important factor to estimate the ADE of plant protein supplement in pigs, and ADF was the best one. （2） The most effective equations were as below： （ 1 ） OE （kJ/kg DM） = 14 741.86 - 185.01ADF＋54.01SCHO＋22.45CP （ R =0.988,RSD= 67.9,P〈0.01 ） ; （2） DE （kJ/kg DM） =22 223.26 -209.58ADF＋26.79SCHO-1.09GE （ Ff =0.989,RSD=66.9, P〈0.01 ） . [Conclusion] The accurate, practical and specific regression equations were established for DE prediction of plant protein supplement in pig.展开更多
In this paper,an application of a nonlinear predictive controller based on a self recurrent wavelet network (SRWN) model for a direct internal reforming solid oxide fuel cell (DIR-SOFC) is presented. As operating temp...In this paper,an application of a nonlinear predictive controller based on a self recurrent wavelet network (SRWN) model for a direct internal reforming solid oxide fuel cell (DIR-SOFC) is presented. As operating temperature and fuel utilization are two important parameters,the SOFC is identified using an SRWN with inlet fuel flow rate,inlet air flow rate and current as inputs,and temperature and fuel utilization as outputs. To improve the operating performance of the DIR-SOFC and guarantee proper operating conditions,the nonlinear predictive control is implemented using the off-line trained and on-line modified SRWN model,to manipulate the inlet flow rates to keep the temperature and the fuel utilization at desired levels. Simulation results show satisfactory predictive accuracy of the SRWN model,and demonstrate the excellence of the SRWN-based predictive controller for the DIR-SOFC.展开更多
Support vector regression (SVR) combined with particle swarm optimization (PSO) for its parameter optimization, was proposed to establish a model to predict the thermal conductivity of polymer-based composites under d...Support vector regression (SVR) combined with particle swarm optimization (PSO) for its parameter optimization, was proposed to establish a model to predict the thermal conductivity of polymer-based composites under different mass fractions of fillers (mass fraction of polyethylene (PE) and mass fraction of polystyrene (PS)). The prediction performance of SVR was compared with those of other two theoretical models of spherical packing and flake packing. The result demonstrated that the estimated errors by leave-one-out cross validation (LOOCV) test of SVR models, such as mean absolute error (MAE) and mean absolute percentage error (MAPE), all are smaller than those achieved by the two theoretical models via applying identical samples. It is revealed that the generalization ability of SVR model is superior to those of the two theoretical models. This study suggests that SVR can be used as a powerful approach to foresee the thermal property of polymer-based composites under different mass fractions of polyethylene and polystyrene fillers.展开更多
文摘[Objective] The aim of the study was to establish the effective and accurate formulas for estimating the digestible energy （DE） values of plant protein supplement in pig. [Method] By difference method with different amount of alternative feeds （20% -50%）, two4 x4 Latin- square-designed trials were taken on eight castrated male pigs [ Yorkshire x Landrace x Neijiang pig, initial body-weight： （46 ±2） kg ] to deter- mine the apparent digestible energy （ADE） of the eight kinds of plant protein supplement commonly used in China, that is, corn gluten meal （sol.）, soybean meal （ sol. ）, fababean, pea, rapeseed meal （ sol. ）, sesame meal （ sol. ）, rapeseed meal （ exp. ） and cotton seed meal （sol.）. [Resultl （1） Fiber was the most important factor to estimate the ADE of plant protein supplement in pigs, and ADF was the best one. （2） The most effective equations were as below： （ 1 ） OE （kJ/kg DM） = 14 741.86 - 185.01ADF＋54.01SCHO＋22.45CP （ R =0.988,RSD= 67.9,P〈0.01 ） ; （2） DE （kJ/kg DM） =22 223.26 -209.58ADF＋26.79SCHO-1.09GE （ Ff =0.989,RSD=66.9, P〈0.01 ） . [Conclusion] The accurate, practical and specific regression equations were established for DE prediction of plant protein supplement in pig.
基金supported by the National High-Tech Research and Devel-opment Program (863) of China (No. 2006AA05Z148)the Shanghai Municipal Natural Science Foundation, China (No. 08ZR1409800)
文摘In this paper,an application of a nonlinear predictive controller based on a self recurrent wavelet network (SRWN) model for a direct internal reforming solid oxide fuel cell (DIR-SOFC) is presented. As operating temperature and fuel utilization are two important parameters,the SOFC is identified using an SRWN with inlet fuel flow rate,inlet air flow rate and current as inputs,and temperature and fuel utilization as outputs. To improve the operating performance of the DIR-SOFC and guarantee proper operating conditions,the nonlinear predictive control is implemented using the off-line trained and on-line modified SRWN model,to manipulate the inlet flow rates to keep the temperature and the fuel utilization at desired levels. Simulation results show satisfactory predictive accuracy of the SRWN model,and demonstrate the excellence of the SRWN-based predictive controller for the DIR-SOFC.
基金supported by the Program for New Century Excellent Talents in University of China (Grant No. NCET-07-0903)the Scientific Research Foundation for the Returned Overseas Chinese Scholars of Ministry of Education, China (Grant No. 2008101-1)+2 种基金the Fundamental Research Funds for the Central Universities (Grant Nos. CDJXS10101107, CDJXS10100037)the Natural Science Foundation of Chongqing, China (Grant No. CSTC2006BB5240)the Innovative Talent Training Project of the Third Stage of "211 Project", Chongqing University (Grant No. S-09109)
文摘Support vector regression (SVR) combined with particle swarm optimization (PSO) for its parameter optimization, was proposed to establish a model to predict the thermal conductivity of polymer-based composites under different mass fractions of fillers (mass fraction of polyethylene (PE) and mass fraction of polystyrene (PS)). The prediction performance of SVR was compared with those of other two theoretical models of spherical packing and flake packing. The result demonstrated that the estimated errors by leave-one-out cross validation (LOOCV) test of SVR models, such as mean absolute error (MAE) and mean absolute percentage error (MAPE), all are smaller than those achieved by the two theoretical models via applying identical samples. It is revealed that the generalization ability of SVR model is superior to those of the two theoretical models. This study suggests that SVR can be used as a powerful approach to foresee the thermal property of polymer-based composites under different mass fractions of polyethylene and polystyrene fillers.
