Comprehensive evaluation method of the poultry house indoor environment based on gray relation analysis and analytic hierarchy process

Study on the changing patterns of production performance of laying hens and their relationships with environmental factors in a large-scale henhouse

The production performance of laying hens is influenced by various environmental factors within the henhouse. The intricate interactions among these factors make the impact process highly complicated. The exact relationships between production performance and environmental variables are still not well understood. In this study, we measured the production performance of laying hens and various environmental variables across different parts of the henhouse, evaluated the weight of each environmental variable, and constructed a laying rate prediction model. Results displayed that body weight, laying rate, egg weight and eggshell thickness of hens decrease gradually from WCA to FA (P < 0.05). Serum levels of FSH and LH, as well as antibody level of H5 Re-13, gradually decrease from WCA to FA (P < 0.05). Moreover, the values for temperature (T), temperature-humidity index (THI), air velocity (AV), carbon dioxide (CO2), and particulate matter (PM2.5) gradually increase from WCA to FA (P < 0.05). Conversely, the relative humidity (RH) value gradually decreases from FA to WCA (P < 0.05). Additionally, the weights of the environmental variables, determined using a combination of the grey relational analysis (GRA) and analytic hierarchy process (AHP), were as follows in descending order: RH, THI, T, light intensity (LI), AV, PM2.5, NH3, and CO2. When the number of decision trees in the laying rate prediction model was set to 2,500, the results displayed a high level of agreement between the model's predictions and the observed outcomes. The model's performance evaluation yielded an R2 value of 0.89995 for the test set, suggesting strong predictive effects. In conclusion, the current study revealed significant differences in both the production performance of laying hens and the environmental variables across different parts of the henhouse. Furthermore, the study demonstrated that different environmental factors have distinct impacts on laying rate, with humidity and temperature identified as the primary factors. Finally, a multi-variable prediction model was constructed, exhibiting high accuracy in predicting laying rate.

Effect of Environmental and Farm-Associated Factors on Live Performance Parameters of Broilers Raised under Commercial Tropical Conditions

Although temperature, relative humidity, and farm-associated factors are known to affect broiler live performance, data about the impact of these variables under commercial operations are still scarce. This study aimed to evaluate the effect of temperature, relative humidity, a thermal humidity index, management, and farm-associated factors on BW, BW gain, feed conversion ratio (FCR), and mortality of broilers raised to 35 d under commercial tropical conditions. The data analyzed included performance records of Ross 308 AP broiler flocks placed between 2018 and 2020. Environmental monitoring information was obtained from electronic sensors that captured data hourly from 80 flocks in 29 farms. Farm-associated factors were gathered using a survey of 86 farms. Three data analyses were conducted in parallel. Correlation analyses, one-way ANOVA, and machine learning techniques were employed. Results indicated that BW and BW gain were reduced, and FCR worsened (p < 0.001) up to 21 d when chickens were mainly exposed to temperatures 2.5 °C lower than the recommended optimums for each age period. At the same time, mortality at 28 and 35 d increased. In conclusion, all farm-associated factors affected chicken live performance. Variable importance analysis indicated that performance results at 14 and 21 d were significant to predict BW at 35. At the same time, sex, distance between the hatchery and farm, and farm altitude accounted for the most significant contributions from the farm-associated factors.

Prediction of wastewater discharge based on GRA-LSTM: a case study of Beijing

Water resources, as one of the indispensable resources for urban development, have become an important factor limiting the sustainable development of cities. In order to promote sustainable urban development, Beijing has set the work task of reaching 99% of urban and rural wastewater treatment rate from 2020 to 2035. Accurate prediction of future wastewater discharge is essential to achieve the target. For this reason, this study takes Beijing as the research object and constructs a combined prediction model based on gray relational analysis and long- and short-term memory (GRA-LSTM). Firstly, gray relational analysis (GRA) is used to analyze the correlation of the experimental data indicators affecting the amount of wastewater discharged in order to obtain experimental data indicators with stronger correlation. Secondly, the long short-term memory (LSTM) model was used to learn the characteristics of the key impact indicators and obtain the optimal model parameters. The results showed that the mean absolute percentage error (MAPE) value of the combined GRA-LSTM model constructed in this study was 5.62%, and the prediction accuracy was higher than that of the other seven prediction models. Then, three scenarios with low, medium, and high dimensions were set to predict the wastewater discharge in Beijing from 2020 to 2035, and the prediction result that the wastewater discharge in Beijing will still continue to grow was obtained. Finally, in order to improve the water utilization rate and promote the sustainable development of the city, this study proposes relevant policy recommendations in terms of the unbalanced urban-rural development of Beijing's wastewater treatment capacity and the increase of recycled water usage.

New energy power system operation security evaluation based on the SWOT analysis

Grid-connection of new energy is highly important in promoting the use of clean and renewable energy. However, it will bring huge risks to the power grid operation security, such as frequency stability, voltage stability, small signal stability, and transient stability, etc.,. In the study, SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis has been employed to construct 24 kinds of internal and external evaluation factors and 8 kinds of improvement strategies, for assessing operation security prospective with new energy power system of HM in China. The weights of SWOT factors are determined with the fuzzy-AHP method. Moreover, the fuzzy-MARCOS approach is used to select the most suitable strategies for power system operation security effective implementation. The reported research reveals that new energy in HM area not only has an ample potential for full development and generating electricity, but also brings operation security problems due to large-scale grid connection. Therefore, 8 kinds of improvement strategies are suggested to encourage the government to exploit and develop new resources, improve the investment pay, power generation and transmission technologies to mitigate the current energy crisis, and increase the energy security for sustainable development of the country. The methodology proposed herein is applicable with a case study concerning the operation security prospective of HM power grid, and all phases of the comparative analysis and sensitivity analysis illustrate the validity of MARCOS method. Furthermore, the ranked order of strategies is obtained as A₂ > A₆ > A₅ > A₁ > A₈ > A₇ > A₄ > A₃. The three most important strategies are A₂, A₆ and A₅, i.e., “improving the technical establishment to encourage efficient and cheap electricity production”, “strive to build local permanent load, and reduce the risk of long-distance and high-capacity transmission”, “taking advantage of government incentives and investment to modify the irrational energy policies and energy planning”, respectively.