Effects of dietary forage neutral detergent fiber and rumen degradable starch ratios on chewing activity, ruminal fermentation, ruminal microbes and nutrient digestibility of Hu sheep fed a pelleted total mixed ration.

Pelleted total mixed ration (P-TMR) feeding, which has become a common practice in providing nutrition for fattening sheep, requires careful consideration of the balance between forage neutral detergent fiber (FNDF) and rumen degradable starch (RDS) to maintain proper rumen functions. The present study aimed to investigate the effects of the dietary FNDF/RDS ratio (FRR) on chewing activity, ruminal fermentation, ruminal microbes, and nutrient digestibility in Hu sheep fed a P-TMR diet. This study utilized eight ruminally cannulated male Hu sheep, following a 4 × 4 Latin square design with 31 d each period. Diets consisted of four FRR levels: 1.0 (high FNDF/RDS ratio, HFRR), 0.8 (middle high FNDF/RDS ratio, MHFRR), 0.6 (middle low FNDF/RDS ratio, MLFRR), and 0.4 (low FNDF/RDS ratio, LFRR). Reducing the dietary FRR levels resulted in a linear decrease in ruminal minimum pH and mean pH, while linearly increasing the duration and area of pH below 5.8 and 5.6, as well as the acidosis index. Sheep in the HFRR and MHFRR groups did not experience subacute ruminal acidosis (SARA), whereas sheep in another two groups did. The concentration of total volatile fatty acid and the molar ratios of propionate and valerate, as well as the concentrate of lactate in the rumen linearly increased with reducing dietary FRR, while the molar ratio of acetate and acetate to propionate ratio linearly decreased. The degradability of NDF and ADF for alfalfa hay has a quadratic response with reducing the dietary FRR. The apparent digestibility of dry matter, organic matter, neutral detergent fiber, and acid detergent fiber linearly decreased when the dietary FRR was reduced. In addition, reducing the dietary FRR caused a linear decrease in OTUs, Chao1, and Ace index of ruminal microflora. Reducing FRR in the diet increased the percentage of reads assigned as Firmicutes, but it decreased the percentage of reads assigned as Bacteroidetes in the rumen. At genus level, the percentage of reads assigned as Prevotella, Ruminococcus, Succinivibrio, and Butyrivibrio linearly decreased when the dietary FRR was reduced. The results of this study demonstrate that the dietary FRR of 0.8 is crucial in preventing the onset of SARA and promotes an enhanced richness of ruminal microbes and also improves fiber digestibility, which is a recommended dietary FRR reference when formulating P-TMR diets for sheep.

Forage neutral detergent fiber (FNDF) and rumen degradable starch (RDS) are key components of carbohydrates in the diet for ruminants, which would reflect saliva secretion and the acid production potential of feed. However, appropriate FNDF to RDS ratios (FRR) applicable to ruminants under the condition of pelleted total mixed ration (P-TMR) feeding have not been reported. In this study, we investigated the effects of the dietary FRR on chewing activity, ruminal fermentation, ruminal microbial communities, and nutrient digestibility of Hu sheep under P-TMR feeding. The results indicate that reducing dietary FRR levels would induce acidosis in sheep, which negatively affected fiber utilization and ruminal bacterial communities. The FRR of 0.8 was a recommended dietary FRR when formulating a P-TMR diet for fattening sheep, as indicated by decreased ruminal acidosis risk and increased richness of ruminal microbes in the rumen as well as nutrient digestibility.

Prediction of inlet SO2 concentration of wet flue gas desulfurization (WFGD) by operation parameters of coal-fired boiler.

Circulating fluidized bed (CFB) boilers with wet flue gas desulfurization (WFGD) system is a popular technology for SO2 removal in the coal-fired thermal power plant. However, the long response time of continues emission monitoring system (CEMS) and the hardness of continuously monitoring the coal properties leads to the difficulties for controlling WFGD. It is important to build a model that is adaptable to the fluctuation of load and coal properties, which can obtain the SO2 concentration ahead CEMS, without relying on coal properties. In this paper, a prediction model of inlet SO2 concentration of WFGD considering the delay between the features and target based on long-short term memory (LSTM) network with auto regression feature is established. The SO2 concentration can be obtained 90 s earlier than CEMS. The model shows good adaptability to the fluctuation of SO2 concentration and coal properties. The root-mean-squared error (RMSE) and R squared (R2) of the model are 30.11 mg/m3 and 0.986, respectively. Meanwhile, a real-time prediction system is built on the 220 t/h unit. A field test for long-term operation has been conducted. The prediction system is able to continuously and accurately predict the inlet SO2 concentration of the WFGD, which can provide the operators with an accurate reference for the control of WFGD.

Dynamic NOx emission prediction based on composite models adapt to different operating conditions of coal-fired utility boilers.

An accurate NOx concentration prediction model plays an important role in low NOx emission control in power stations. Predicting NOx in advance is of great significance in satisfying stringent environmental policies. This study aims to accurately predict the NOx emission concentration at the outlet of boilers on different operating conditions to support the DeNOx procedure. Through mutual information analysis, suitable features are selected to build models. Long short-term memory (LSTM) models are utilized to predict NOx concentration at the boiler's outlet from selected input features and exhibit power in fitting multivariable coupling, nonlinear, and large time-delay systems. Moreover, a composite LSTM model composed of models on different operating conditions, like steady-state and transient-state condition, is prosed. Results of one whole day of typical operating data show that the accuracy of the NOx concentration and fluctuation trend prediction based on this composite model is superior to that using a single LSTM model and other non-time-sequence models. The root mean square error (RMSE) and R2 of the composite LSTM model are 3.53 mg/m3 and 0.89, respectively, which are better than those of a single LSTM (i.e., 5.50 mg/m3 and 0.78, respectively).