Spatiotemporal attention mechanism-based deep network for critical parameters prediction in chemical process

Zhuang, Yuan; Yang, Zhe; Ling, Yiqun; Wu, Chuanpeng; Li, Chuankun

doi:10.1016/j.psep.2021.09.024

Cited by 17 publications

(2 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Peng et al established an LSTM-AM model by incorporating an attention mechanism (AM) into LSTM to predict the future trend of process parameters [84]. Considering the impact of different input features at different times on output, Zhu et al proposed a hybrid model that integrates the spatiotemporal attention (STA) mechanism, CNN, and BiLSTM to predict the trend of key parameter changes [85]. Xiang et al proposed a method for predicting the mid-term trends of key process parameters using small datasets [86].…”

Section: Early Prediction and Warningmentioning

confidence: 99%

Abnormal Situation Management in Chemical Processes: Recent Research Progress and Future Prospects

et al. 2023

View full text Add to dashboard Cite

In the chemical process, abnormal situations are precursor events of incidents and accidents. Abnormal situation management (ASM) can effectively identify abnormalities and prevent them from evolving into incidents or accidents, ensuring the safe and smooth operation of chemical plants. In recent years, ASM has attracted extensive attention from the process industry and from academia, and a lot of research work has been conducted. However, the intelligence level of ASM in actual chemical plants is still relatively low, and industrial applications still face many difficulties and challenges. This review first summarizes the concepts and involved in the contents of ASM. Then, the latest research progress in various aspects of ASM is systematically reviewed. Finally, the challenges and future research directions of ASM are analyzed based on the perspective of industrial application. This review aims to provide the most cutting-edge reference for follow-up research on ASM, and to promote the intelligent development and practical industrial application of ASM in the chemical process.

show abstract

Section: Early Prediction and Warningmentioning

confidence: 99%

Abnormal Situation Management in Chemical Processes: Recent Research Progress and Future Prospects

et al. 2023

View full text Add to dashboard Cite

show abstract

“…For green ammonia production, the high-dimensional time-series data with a frequency of seconds in units of years is influenced by the characteristics of renewable energy, which aggravates the time lag phenomenon in the process control and optimization. Current existing models ,− preliminarily focus on single-step forecasting; hence, it remains difficult to achieve the efficiency requirements of industrial control and optimization applications, and a large time cost is incurred in calculations because of the LSTM’s step-by-step serial mode.…”

Section: Introductionmentioning

confidence: 99%

Time-Series Forecasting of Large-Scale Green Ammonia Production: An Intelligent Dynamic Modeling Case in the Inner Mongolia Area of China

Zhang,

Li,

Zhang

et al. 2024

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

Renewable energy sources have been viewed as an important approach to solve the energy crisis and are carbon neutral. Compared with other storage mediums, green ammonia has the highest total energy efficiency and is an important raw material in the chemical industry. The fluctuation of renewable energy is a huge challenge for green ammonia production; thus, a high-precision prediction model is required to enable the building of an advanced control system for green ammonia production. In this study, a transformer-based multivariable multistep time-series prediction model that includes the temporal scales feature of renewable energy, called MSPTST, is proposed as the first step to solve the problem of unstable green ammonia production. The model outperformed other state-of-theart models, and its R 2 values were 0.986, 0.9998, and 0.990 under a high load condition, low renewable energy condition, and natural condition, respectively. Stable operation and swift load transitions are facilitated through a model predictive control based on the MSPTST framework. The impact of energy fluctuations on green ammonia synthesis is addressed by further integrating control theory and algorithms.

show abstract

Hybrid Deep Learning Model Integrating Attention Mechanism for the Accurate Prediction and Forecasting of the Cryptocurrency Market

Saqware,

2024

Oper. Res. Forum

View full text Add to dashboard Cite

Spatiotemporal attention mechanism-based deep network for critical parameters prediction in chemical process

Cited by 17 publications

References 43 publications

Abnormal Situation Management in Chemical Processes: Recent Research Progress and Future Prospects

Abnormal Situation Management in Chemical Processes: Recent Research Progress and Future Prospects

Time-Series Forecasting of Large-Scale Green Ammonia Production: An Intelligent Dynamic Modeling Case in the Inner Mongolia Area of China

Hybrid Deep Learning Model Integrating Attention Mechanism for the Accurate Prediction and Forecasting of the Cryptocurrency Market

Contact Info

Product

Resources

About