resid

简明释义

英[ˈrezɪd]美[rɪˈzɪd]

n. 渣油；残油

n. （Resid）人名；（西）雷西德

英英释义

单词用法

同义词

反义词

例句

1.Emphases resid in the problems of choosing an efficient color space, deciding threshold and matching feature points.

重点研究了颜色空间的选取、目标识别中确定阀值的策略及特征点的选取方法。

2.The recent advances in hydrocracking, mild hydrocracking and resid hydrocracking and hydrotreating technologies are reviewed in this paper.

本文重点评述了馏分油加氢裂化、缓和加氢裂化、渣油加氢裂化、加氢精制和加氢处理技术的新进展。

3.Increases in throughput, resid blending ratio and gasoline octane as well as drop in coke yield can be obtained after the catalyst mixing.

使用混兑催化剂可提高渣油掺炼比，降低焦炭产率，提高装置处理量，并可提高汽油辛烷值。

4.For FCC resid processing, USY zeolite is perferred because of its superior hydrothermal stability, vanadium resistance and coke selectivity compared to conventional zeolites.

超稳y分子筛的水热稳定性、抗钒性能和焦炭选择性都优于传统的沸石，适合作为渣油FCC催化剂的活性主体。

5.The factors influencing coking within resid fluid catalytic cracking(RFCC) disengagers and the mechanism are analyzed.

分析了重油催化裂化（RFCC）沉降器结焦的影响因素和机理。

6.For FCC resid processing, USY zeolite is perferred because of its superior hydrothermal stability, vanadium resistance and coke selectivity compared to conventional zeolites.

超稳y分子筛的水热稳定性、抗钒性能和焦炭选择性都优于传统的沸石，适合作为渣油FCC催化剂的活性主体。

7.The environmental report indicated that the resid 残留物 could pose a risk to local wildlife.

环境报告指出，resid 残留物可能对当地野生动物构成风险。

8.After the reaction, we need to remove the resid 残留物 from the flask.

反应后，我们需要从烧瓶中去除resid 残留物。

9.In the lab, we often analyze the resid 残留物 left after chemical reactions.

在实验室，我们经常分析化学反应后留下的resid 残留物。

10.The oil refinery had to deal with excess resid 残油 during the processing.

炼油厂在加工过程中不得不处理过量的resid 残油。

11.The chemical analysis showed a high level of resid 残留物 in the sample.

化学分析显示样本中有高水平的resid 残留物。

作文

The concept of residuals is fundamental in various fields, especially in statistics and data analysis. When we conduct a regression analysis, we often focus on how well our model fits the data. However, understanding the residuals, which are the differences between observed values and the values predicted by our model, is equally important. These residuals can provide insights into the accuracy of our predictions and help identify patterns that our model may have missed. For instance, if the residuals show a systematic pattern, it indicates that the model is not capturing some underlying relationship in the data, prompting us to refine our approach. In practical applications, the analysis of residuals can be crucial in fields such as economics, engineering, and social sciences. In economics, policymakers rely on regression models to forecast economic indicators. By examining the residuals from these models, they can gauge whether their predictions are reliable or if adjustments are necessary. Similarly, engineers use regression techniques to optimize designs and processes. The residuals help them understand the variability in their measurements and improve their models accordingly.Moreover, the importance of residuals extends beyond mere statistical analysis. They play a significant role in machine learning as well. In supervised learning, the objective is to minimize the residuals during the training process, ensuring that the model generalizes well to unseen data. This is achieved through techniques such as regularization, which penalizes large residuals and discourages overfitting. In summary, the term resid refers to the residuals in statistical modeling, representing the discrepancies between observed and predicted values. Understanding residuals is essential for evaluating the performance of models across various disciplines. It allows analysts and researchers to refine their approaches, improve accuracy, and ultimately make better-informed decisions based on their findings. As we continue to develop more sophisticated models and algorithms, the analysis of residuals will remain a critical component in ensuring the reliability and validity of our predictions. In conclusion, the study of residuals is not just an academic exercise; it is a practical necessity in many professional fields. By paying attention to these important metrics, we can enhance our understanding of complex systems and make more accurate forecasts. Whether in economics, engineering, or machine learning, the implications of residual analysis are vast and impactful, shaping the way we interpret data and make decisions.

‘resid’是残差的意思，在统计学和数据分析等多个领域是一个基本概念。当我们进行回归分析时，通常会关注我们的模型与数据的拟合程度。然而，理解residual，即观察值与模型预测值之间的差异，同样重要。这些residual可以提供有关我们预测准确性的见解，并帮助识别模型可能遗漏的模式。例如，如果residual显示出系统性模式，这表明模型没有捕捉到数据中的某些潜在关系，促使我们优化方法。在实际应用中，residual的分析在经济学、工程学和社会科学等领域至关重要。在经济学中，政策制定者依赖回归模型来预测经济指标。通过检查这些模型的residual，他们可以评估其预测是否可靠，或者是否需要调整。同样，工程师使用回归技术来优化设计和过程。residual帮助他们理解测量中的变异性，并相应地改进模型。此外，residual的重要性不仅限于统计分析。在机器学习中，它们也扮演着重要角色。在监督学习中，目标是在训练过程中最小化residual，确保模型能够很好地泛化到未见过的数据。这是通过正则化等技术实现的，正则化会惩罚大的residual并抑制过拟合。总之，术语resid指的是统计建模中的残差，代表观察值和预测值之间的差异。理解residual对于评估各个学科模型的表现至关重要。它使分析师和研究人员能够优化其方法，提高准确性，并最终根据其发现做出更明智的决策。随着我们继续开发更复杂的模型和算法，residual分析将始终是确保我们预测的可靠性和有效性的关键组成部分。最后，residual的研究不仅仅是学术练习；在许多专业领域中，它是一个实际必要。通过关注这些重要指标，我们可以增强对复杂系统的理解，并做出更准确的预测。无论是在经济学、工程学还是机器学习中，residual分析的影响都是广泛而深远的，塑造了我们对数据的解释和决策方式。