Data-driven prediction and control of wastewater treatment process through the combination of convolutional neural network and recurrent neural network

Guo, Zhiwei; Du, Boxin; Wang, Jianhui; Shen, Yu; Li, Qiao; Ding, Feng; Gao, Xu; Wang, Heng

doi:10.1039/d0ra00736f

Cited by 30 publications

(13 citation statements)

References 45 publications

(49 reference statements)

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“…When there is only one correlated variable, the network is designed similarly to the y-y model, except that the CCA algorithm is selected for determining the time window. Considering the case of multiple correlated variables, the variables with the number of interrelationships greater than a threshold are first chosen as the correlated variables using the CCA algorithm [29]. en the maximum time lag T of these correlated variables is selected and multiplied by the correction factor k as the value of the time window length s. For the network structure, this paper proposes to change the Computational Intelligence and Neuroscience input width to 1 by selecting a rectangular convolutional kernel with a width equal to the input dimension in the first convolutional layer.…”

Section: Model Design Of the Agricultural Economy Forecasting Methods...mentioning

confidence: 99%

Deep Intelligence-Driven Efficient Forecasting for the Agriculture Economy of Computational Social Systems

Ji-yu

2022

Computational Intelligence and Neuroscience

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In the vision of smart cities, everything is highly connected with the aid of computational intelligence. Therefore, the cyber-physical society has been named a computational social system for a long time. Due to the high relation with vast populations’ national livelihood, agriculture will still serve as a core industry in the national economy. As a result, this study focused on an efficient forecasting method for the agriculture economy. In recent years, the conception of deep intelligence has received overall prevalence in academia because of its excellent performance in implementing intelligent information processing tasks. Hence, this paper utilized deep intelligence driven by neural networks and managed to investigate an efficient prediction method for the agriculture economy of computational social systems. To fit the time-series forecasting scene of the long-term development of the agriculture economy, the convolutional neural network model is slightly improved by revising its parallel structure into the recurrent format. Finally, simulations on realistic datasets are carried out to evaluate the proposed forecasting method.

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Section: Model Design Of the Agricultural Economy Forecasting Methods...mentioning

confidence: 99%

Deep Intelligence-Driven Efficient Forecasting for the Agriculture Economy of Computational Social Systems

Ji-yu

2022

Computational Intelligence and Neuroscience

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“…[ 133 ] There are some industry specific software developments (for real‐time commercial scale process control) as well, such as MCGS, [ 215 ] Advantech, [ 202,218 ] CMG‐STARS, [ 182 ] and PLC. [ 113 ] Finally, some studies also examined the effectiveness of different software, such as MODELICA, [ 214 ] gPROMS/gOPT, [ 124 ] ThinkSpeak, [ 265 ] HYSYS, [ 172 ] PROTEUS, [ 246 ] Arduino (for real‐time implementations), [ 279 ] Python, [ 288,289 ] and NAL. [ 46 ]…”

Section: Trends In Ai‐based Control Applicationsmentioning

confidence: 99%

Artificial intelligence‐based process control in chemical, biochemical, and biomedical engineering

Dutta

Upreti

2021

Can J Chem Eng

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In the last three decades, artificial intelligence (AI) has been increasingly and vigorously utilized for process control in chemical, biochemical, and biomedical engineering. These disciplines frequently involve fairly sophisticated processes under risks of operational upsets, and thus have an ever increasing demand of superior control strategies. As the deployment of AI in process control pushes the limits in this regard, the research advances, which are multitudinous and varied, need to be assimilated and organized to help promote further utilization and progress in the field. To that end, we examine more than 280 relevant research publications, and systematically collate the information. The AI‐based technologies are classified, and their over‐arching control paradigm is presented. Common AI‐based control technologies are then presented, which are based on expert systems, fuzzy logic, artificial neural networks, nature‐inspired algorithms, and hybrid approaches. Their working principles, types, and implementations are summarized along with advantages, limitations, and comparisons, if available. Important applications in the above disciplines are included with the help of tables that capture important details. A discussion is also provided on advanced and newly emerging AI‐based control technologies with pertinent applications. Overall trends are analyzed, and future prospects are identified based on the survey.

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“…Among Equations (6)(7)(8), 𝑊 𝑖 and 𝑊 𝑐 are the IG connection weight and the 𝑡𝑎𝑛ℎ layer weight, respectively;…”

Section: Bidirectional Long Short-term Memory Modelingmentioning

confidence: 99%

“…This gives rise to the exploration of new techniques or management patterns in related fields, the most typical of which is the fuzzy sewage treatment process (FSTP) [3][4][5][6]. The FSTP is actually a kind of typical and complex industrial scenarios, in which treatment process is implemented accompanied with much fuzziness and uncertainty [7][8][9]. Owing to its importance to achieving green ecosystem, the realization of its optimal management is highly correlated to the sustainable engineering of cities [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Data-driven management for fuzzy sewage treatment processes using hybrid neural computing

Zeng

Guo

Shen

et al. 2021

Neural Comput & Applic

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Data-driven prediction and control of wastewater treatment process through the combination of convolutional neural network and recurrent neural network

Abstract: This work proposes a novel data-driven mechanism for prediction of wastewater treatment results through mixture of two neural network models.

Cited by 30 publications

References 45 publications

Deep Intelligence-Driven Efficient Forecasting for the Agriculture Economy of Computational Social Systems

Deep Intelligence-Driven Efficient Forecasting for the Agriculture Economy of Computational Social Systems

Artificial intelligence‐based process control in chemical, biochemical, and biomedical engineering

Data-driven management for fuzzy sewage treatment processes using hybrid neural computing

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