We prove that non-recursive base conversion can always be implemented by using a deterministic Markov process. Our paper discusses the pros and cons of recursive and non-recursive methods, in general. And we include a...We prove that non-recursive base conversion can always be implemented by using a deterministic Markov process. Our paper discusses the pros and cons of recursive and non-recursive methods, in general. And we include a comparison between non-recursion and a deterministic Markov process, proving that the Markov process is twice as efficient.展开更多
Model Predictive Control (MPC) has recently found wide acceptance in the process industry, but existing design and implementation methods are restricted to linear process models. A chemical process, however, involves ...Model Predictive Control (MPC) has recently found wide acceptance in the process industry, but existing design and implementation methods are restricted to linear process models. A chemical process, however, involves severe nonlinearity which cannot be ignored in practice. This paper aims to solve this nonlinear control problem by extending MPC to accommodate nonlinear models. It develops an analytical framework for nonlinear model predictive control (NMPC). It also offers a third-order Volterra series based nonparametric nonlinear modelling technique for NMPC design, which relieves practising engineers from the need for deriving a physical-principles based model first. An on-line realisation technique for implementing NMPC is then developed and applied to a Mitsubishi Chemicals polymerisation reaction process. Results show that this nonlinear MPC technique is feasible and very effective. It considerably outperforms linear and low-order Volterra model based methods. The advantages of the developed approach lie not only in control performance superior to existing NMPC methods, but also in eliminating the need for converting an analytical model and then convert it to a Volterra model obtainable only up to the second order. Keywords Model predictive control - Volterra series - process control - nonlinear control Yun Li is a senior lecturer at University of Glasgow, UK, where has taught and researched in evolutionary computation and control engineering since 1991. He worked in the UK National Engineering Laboratory and Industrial Systems and Control Ltd, Glasgow in 1989 and 1990. In 1998, he established the IEEE CACSD Evolutionary Computation Working Group and the European Network of Excellence in Evolutionary Computing (EvoNet) Workgroup on Systems, Control, and Drives. In summer 2002, he served as a visiting professor to Kumamoto University, Japan. He is also a visiting professor at University of Electronic Science and Technology of China. His research interests are in parallel processing, design automation and di展开更多
Substantial environmental and economic benefits can be achieved by recycling used lithium-ion batteries. Hydrometallurgy is often employed to recover waste LiNi_(x)Co_(y)Mn_(z)O_(2) cathode materials. As Ni, Co and Mn...Substantial environmental and economic benefits can be achieved by recycling used lithium-ion batteries. Hydrometallurgy is often employed to recover waste LiNi_(x)Co_(y)Mn_(z)O_(2) cathode materials. As Ni, Co and Mn are transition metals, they exhibit similar properties;therefore, separating them is difficult. Thus, most researchers have focused on leaching processes, while minimal attention has been devoted to the separation of valuable metals from waste LiNi_(x)Co_(y)Mn_(z)O_(2) cathode materials. Herein, we propose an environment-friendly, gentle process involving the usage of pyrometallurgy and hydrometallurgy to gradually leach valuable metals and effectively separate them. Interestingly, Li is recovered through a reduction roasting and water leaching process using natural graphite powder, Ni and Co are recovered through ammonia leaching and extraction processes and Mn is recovered through acid leaching and evaporation–crystallization processes. Results show that ~87% Li, 97.01% Co, 97.08% Ni and 99% Mn can be leached using water, ammonia and acid leaching processes. The result obtained using the response surface methodology shows that the concentration of (NH4)2SO3 is a notable factor affecting the leaching of transition metals. Under optimal conditions, ~97.01% Co, 97.08% Ni and 0.64% Mn can be leached out. The decomposition of LiNi_(x)Co_(y)Mn_(z)O_(2) is a two-step process. This study provides valuable insights to develop an environment-friendly, gentle leaching process for efficiently recycling valuable metals, which is vital for the lithium-ion battery recycling industry.展开更多
Long-memory process has been widely studied in classical financial time series analysis,which has merely been reported in the field of interval-valued financial time series.The aim of this paper is to explore long-mem...Long-memory process has been widely studied in classical financial time series analysis,which has merely been reported in the field of interval-valued financial time series.The aim of this paper is to explore long-memory process in the prediction of interval-valued time series(IvTS).To model the long-memory process,two novel interval-valued time series prediction models named as interval-valued vector autoregressive fractionally integrated moving average(IV-VARFIMA)and ARFIMAX-FIGARCH were established.In the developed long-memory pattern,both of the short term and long-term influences contained in IvTS can be included.As an application of the proposed models,interval-valued form of WTI crude oil futures price series is predicted.Compared to current IvTS prediction models,IV-VARFIMA and ARFIMAX-FIGARCH can provide better in-sample and out-of-sample forecasts.展开更多
文摘We prove that non-recursive base conversion can always be implemented by using a deterministic Markov process. Our paper discusses the pros and cons of recursive and non-recursive methods, in general. And we include a comparison between non-recursion and a deterministic Markov process, proving that the Markov process is twice as efficient.
文摘Model Predictive Control (MPC) has recently found wide acceptance in the process industry, but existing design and implementation methods are restricted to linear process models. A chemical process, however, involves severe nonlinearity which cannot be ignored in practice. This paper aims to solve this nonlinear control problem by extending MPC to accommodate nonlinear models. It develops an analytical framework for nonlinear model predictive control (NMPC). It also offers a third-order Volterra series based nonparametric nonlinear modelling technique for NMPC design, which relieves practising engineers from the need for deriving a physical-principles based model first. An on-line realisation technique for implementing NMPC is then developed and applied to a Mitsubishi Chemicals polymerisation reaction process. Results show that this nonlinear MPC technique is feasible and very effective. It considerably outperforms linear and low-order Volterra model based methods. The advantages of the developed approach lie not only in control performance superior to existing NMPC methods, but also in eliminating the need for converting an analytical model and then convert it to a Volterra model obtainable only up to the second order. Keywords Model predictive control - Volterra series - process control - nonlinear control Yun Li is a senior lecturer at University of Glasgow, UK, where has taught and researched in evolutionary computation and control engineering since 1991. He worked in the UK National Engineering Laboratory and Industrial Systems and Control Ltd, Glasgow in 1989 and 1990. In 1998, he established the IEEE CACSD Evolutionary Computation Working Group and the European Network of Excellence in Evolutionary Computing (EvoNet) Workgroup on Systems, Control, and Drives. In summer 2002, he served as a visiting professor to Kumamoto University, Japan. He is also a visiting professor at University of Electronic Science and Technology of China. His research interests are in parallel processing, design automation and di
基金supported by supported by Yunnan Major Scientific and Technological Projects(China)(No.202202AG050003).
文摘Substantial environmental and economic benefits can be achieved by recycling used lithium-ion batteries. Hydrometallurgy is often employed to recover waste LiNi_(x)Co_(y)Mn_(z)O_(2) cathode materials. As Ni, Co and Mn are transition metals, they exhibit similar properties;therefore, separating them is difficult. Thus, most researchers have focused on leaching processes, while minimal attention has been devoted to the separation of valuable metals from waste LiNi_(x)Co_(y)Mn_(z)O_(2) cathode materials. Herein, we propose an environment-friendly, gentle process involving the usage of pyrometallurgy and hydrometallurgy to gradually leach valuable metals and effectively separate them. Interestingly, Li is recovered through a reduction roasting and water leaching process using natural graphite powder, Ni and Co are recovered through ammonia leaching and extraction processes and Mn is recovered through acid leaching and evaporation–crystallization processes. Results show that ~87% Li, 97.01% Co, 97.08% Ni and 99% Mn can be leached using water, ammonia and acid leaching processes. The result obtained using the response surface methodology shows that the concentration of (NH4)2SO3 is a notable factor affecting the leaching of transition metals. Under optimal conditions, ~97.01% Co, 97.08% Ni and 0.64% Mn can be leached out. The decomposition of LiNi_(x)Co_(y)Mn_(z)O_(2) is a two-step process. This study provides valuable insights to develop an environment-friendly, gentle leaching process for efficiently recycling valuable metals, which is vital for the lithium-ion battery recycling industry.
基金supported by the Humanities and Social Sciences Research Youth Project of the Ministry of Education of China under Grant No.21YJCZH148the Natural Science Foundation of Anhui Province under Grant Nos.2108085MG239,2108085QG290,2008085QG334,and 2008085MG226+2 种基金the National Natural Science Foundation of China under Grant Nos.72001001,71901001,and 72071001the Provincial Natural Science Research Project of Anhui Colleges,China under Grant No.KJ2020A0004The teacher project of Anhui Ecology and Economic Development Research Center in 2021 under Grant No.AHST2021002.
文摘Long-memory process has been widely studied in classical financial time series analysis,which has merely been reported in the field of interval-valued financial time series.The aim of this paper is to explore long-memory process in the prediction of interval-valued time series(IvTS).To model the long-memory process,two novel interval-valued time series prediction models named as interval-valued vector autoregressive fractionally integrated moving average(IV-VARFIMA)and ARFIMAX-FIGARCH were established.In the developed long-memory pattern,both of the short term and long-term influences contained in IvTS can be included.As an application of the proposed models,interval-valued form of WTI crude oil futures price series is predicted.Compared to current IvTS prediction models,IV-VARFIMA and ARFIMAX-FIGARCH can provide better in-sample and out-of-sample forecasts.
