Junfei Qiao – författare

Small Sample Modelling Based on Deep and Broad Forest Regression: Theory and Industrial Application delves into tree-structured methods in the industrial sector, encompassing classical ensemble learning, tree-structured deep forest classification, and broad learning systems with neural networks. It introduces an innovative deep/broad learning algorithm for small-sample industrial modeling tasks. The book is divided into two parts: methodology and practical application in dioxin emission modeling. Methodology sections include Preliminaries, Deep Forest Regression, Broad Forest Regression, and Fuzzy Forest Regression. The application part focuses on modeling dioxin emissions in municipal solid waste incineration. Throughout, various tree-structured strategies are presented, and the authors provide software systems for validating these methods. This book is suitable for advanced undergraduates, graduate engineering students, and practicing engineers looking for self-study resources.Introduces a novel deep and broad regression algorithm specifically designed for small sample industrial modeling. It covers Deep Forest Regression for Industrial Modeling, Broad Forest Regression for Industrial Modeling, and Fuzzy Forest Regression for Industrial ModelingDelves into recent results concerning the hot topic of deep and broad learning using non-neuron units for regression and the interpretability of fuzzy trees. These innovative methods are supported by the use of multi-dimensional benchmark data, providing solid confirmationOffers a real application case for industrial modeling by focusing on dioxin emission concentration. This case revolves around a strict controlled environment index of the municipal solid waste incineration (MSWI) process. The book provides offline modeling techniques such as improved deep forest regression and simplified deep forest regression

Small Sample Modelling Based on Deep and Broad Forest Regression: Theory and Industrial Application delves into tree-structured methods in the industrial sector, encompassing classical ensemble learning, tree-structured deep forest classification, and broad learning systems with neural networks. It introduces an innovative deep/broad learning algorithm for small-sample industrial modeling tasks. The book is divided into two parts: methodology and practical application in dioxin emission modeling. Methodology sections include Preliminaries, Deep Forest Regression, Broad Forest Regression, and Fuzzy Forest Regression. The application part focuses on modeling dioxin emissions in municipal solid waste incineration. Throughout, various tree-structured strategies are presented, and the authors provide software systems for validating these methods. This book is suitable for advanced undergraduates, graduate engineering students, and practicing engineers looking for self-study resources. - Introduces a novel deep and broad regression algorithm specifically designed for small sample industrial modeling. It covers Deep Forest Regression for Industrial Modeling, Broad Forest Regression for Industrial Modeling, and Fuzzy Forest Regression for Industrial Modeling- Delves into recent results concerning the hot topic of deep and broad learning using non-neuron units for regression and the interpretability of fuzzy trees. These innovative methods are supported by the use of multi-dimensional benchmark data, providing solid confirmation- Offers a real application case for industrial modeling by focusing on dioxin emission concentration. This case revolves around a strict controlled environment index of the municipal solid waste incineration (MSWI) process. The book provides offline modeling techniques such as improved deep forest regression and simplified deep forest regression

This book by leading experts consolidates knowledge on the numerical simulation and AI-driven modeling of multiple points dioxins concentration in the municipal solid waste incineration (MSWI) process to provide readers with the skills to develop safer and more efficient waste management practices.The authors explore the complex interplay between MSW management and environmental health, specifically focusing on the generation and production of dioxins during incineration. This comprehensive work amalgamates theoretical insights and practical applications, leveraging numerical simulations and artificial intelligence techniques to offer innovative modeling solutions. It addresses the critical problem of dioxin generation and emissions from MSWI through a structured approach that integrates exhaustive research data, case studies, and advanced computational methodologies that provides an integrated model that accounts for various operational parameters and their effects on emissions. It both discusses the existing theoretical frameworks and empirical studies and applies advanced modeling techniques to yield practical insights. Readers will find clear representations of simulation processes alongside discussions on optimization and control mechanisms. This facilitates an understanding of the underlying mechanisms of dioxin formation, enabling targeted solutions that are informed by both experimental results and advanced simulation techniques. This ultimately furnishes practitioners with robust tools to better understand and mitigate the risks associated with dioxin emissions so they can contribute to safer and more efficient waste management practices.This is an essential resource for engineers, policy-makers, and researchers involved in sustainable waste management and environmental protection. In particular, environmental engineers, MSWI plant operators, and researchers will benefit from the integrated theoretical and practical approach and discussion of real-world scenarios and applications.

An expert discussion of intelligent optimization control in complex industrial processes In A Hardware-in-Loop Digital Twin Approach for Intelligent Optimization of Municipal Solid Waste Incineration: AI and Its Application to Complex Industrial Processes, a team of distinguished researchers delivers an innovative new approach to integrating virtual mechanism data generated through coupled numerical simulation and orthogonal experimental design with real historical data. The book explains how to create a heterogenous ensemble prediction model for carbon monoxide emissions in municipal solid waste incineration (MSWI) processes. The authors focus on intelligent optimization control of MSWI processes based on hardware-in-loop DT platforms. They demonstrate AI-driven modeling, control, optimization algorithms in real-world applications, including virtual-real data hybrid-driven deep modeling and intelligent optimal controls based on multiple objectives. Additional topics include: A thorough introduction to numerical simulation modeling of whole industrial processesComprehensive explorations of the design, implementation, and validation of hardware-in-loop digital twin platformsPractical discussions of AI-driven modeling, control, and optimizationFulsome descriptions of the skills required to address challenges posed by complex industrial processesPerfect for environmental engineers and researchers, A Hardware-in-Loop Digital Twin Approach for Intelligent Optimization of Municipal Solid Waste Incineration will also benefit MSWI plant operators and managers, as well as AI and machine learning researchers and developers of environmental monitoring and control systems.

An expert discussion of intelligent optimization control in complex industrial processes In A Hardware-in-Loop Digital Twin Approach for Intelligent Optimization of Municipal Solid Waste Incineration: AI and Its Application to Complex Industrial Processes, a team of distinguished researchers delivers an innovative new approach to integrating virtual mechanism data generated through coupled numerical simulation and orthogonal experimental design with real historical data. The book explains how to create a heterogenous ensemble prediction model for carbon monoxide emissions in municipal solid waste incineration (MSWI) processes. The authors focus on intelligent optimization control of MSWI processes based on hardware-in-loop DT platforms. They demonstrate AI-driven modeling, control, optimization algorithms in real-world applications, including virtual-real data hybrid-driven deep modeling and intelligent optimal controls based on multiple objectives. Additional topics include: A thorough introduction to numerical simulation modeling of whole industrial processesComprehensive explorations of the design, implementation, and validation of hardware-in-loop digital twin platformsPractical discussions of AI-driven modeling, control, and optimizationFulsome descriptions of the skills required to address challenges posed by complex industrial processes Perfect for environmental engineers and researchers, A Hardware-in-Loop Digital Twin Approach for Intelligent Optimization of Municipal Solid Waste Incineration will also benefit MSWI plant operators and managers, as well as AI and machine learning researchers and developers of environmental monitoring and control systems.