There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

As the environment preservation gradually becomes a matter of major social concern and more strict legislation is being imposed on effluent discharge, more effective processes are required to deal with non-readily biodegradable and toxic pollutants. Synthetic organic dyes in industrial effluents cannot be destroyed in conventional wastewater treatment and consequently, an urgent challenge is the development of new environmentally benign technologies able to mineralize completely these non-biodegradable compounds. This review aims to increase the knowledge on the electrochemical methods used at lab and pilot plant scale to decontaminate synthetic and real effluents containing dyes, considering the period from 2009 to 2013, as an update of our previous review up to 2008. Fundamentals and main applications of electrochemical advanced oxidation processes and the other electrochemical approaches are described. Typical methods such as electrocoagulation, electrochemical reduction, electrochemical oxidation and indirect electro-oxidation with active chlorine species are discussed. Recent advances on electrocatalysis related to the nature of anode material to generate strong heterogeneous OH as mediated oxidant of dyes in electrochemical oxidation are extensively examined. The fast destruction of dyestuffs mediated with electrogenerated active chlorine is analyzed. Electro-Fenton and photo-assisted electrochemical methods like photoelectrocatalysis and photoelectro-Fenton, which destroy dyes by heterogeneous OH and/or homogeneous OH produced in the solution bulk, are described. Current advantages of the exposition of effluents to sunlight in the emerging photo-assisted procedures of solar photoelectrocatalysis and solar photoelectro-Fenton are detailed. The characteristics of novel combined methods involving photocatalysis, adsorption, nanofiltration, microwaves and ultrasounds among others and the use of microbial fuel cells are finally discussed.

Decontamination of wastewaters containing synthetic organic dyes by electrochemical methods. An updated review

This article surveyed the major results in iterative learning control (ILC) analysis and design over the past two decades. Problems in stability, performance, learning transient behavior, and robustness were discussed along with four design techniques that have emerged as among the most popular. The content of this survey was selected to provide the reader with a broad perspective of the important ideas, potential, and limitations of ILC. Indeed, the maturing field of ILC includes many results and learning algorithms beyond the scope of this survey. Though beginning its third decade of active research, the field of ILC shows no sign of slowing down.

A survey of iterative learning control

https://www.jpcomplex.ir/Content/media/image/2013/08/772_orig.pdf

Electrochemical technologies in wastewater treatment

Data-based process monitoring has become a key technology in process industries for safety, quality, and operation efficiency enhancement. This paper provides a timely update review on this topic. First, the natures of different industrial processes are revealed with their data characteristics analyzed. Second, detailed terminologies of the data-based process monitoring method are illustrated. Third, based on each of the main data characteristics that exhibits in the process, a corresponding problem is defined and illustrated, with review conducted with detailed discussions on connection and comparison of different monitoring methods. Finally, the relevant research perspectives and several promising issues are highlighted for future work.

Review of Recent Research on Data-Based Process Monitoring

Survey on iterative learning control, repetitive control, and run-to-run control

Multivariate statistical methods such as principal component analysis (PCA) and partial least square (PLS) have been successfully used in modeling multivariable continuous processes (Kaspar and Ray, 1992; Kourti and MacGregor, 1995; Chen and McAvoy, 1998) Several extensions of the conventional PCA/PLS to batch processes have also been reported, among which multiway PCA (MPCA) model is the most widely used (Wold et al, 1987; Nomikos and MacGregor, 1994, 1995; Wise et al, 1999; Smilde, 2001) The MPCA model is ill-suited for multistage batch processes, as it takes the entire batch data as a single object, and it is difficult to reveal the changes of process correlation from stage to stage Considering that the multiplicity of the operation stage is an inherent nature of many batch processes, each stage has its own underlying characteristics and the process can exhibit significantly different behaviors over different operation stages; it is desirable to develop a stage-based model that can reflect the inherent process stage nature to improve the process understanding and monitoring efficiency Kosanovich et al (1994) and Dong and McAvoy (1995) developed two MPCA/nonlinear MPCA models, utilizing the two-stage nature of a jacketed exothermic batch chemical reactor Their results show that the two-stage models are more powerful than a single model Their stage models, however, inherit the common weakness of the MPCA model that the unavailable future data in an evolving batch should be estimated for on-line monitoring A new stage-based sub-PCA modeling method is proposed in this article for multistage batch processes, based on the recognition of the following: (1) a batch process may be divided into several “operation” stages reflecting its inherent process correlation nature; (2) despite that the process may be time varying, the correlation of its variables will be largely similar within the same “operation” stage Changes in the correlation may be used to indicate changes in the process “operation” stages We have placed a quotation mark around “operation” to indicate that the operation referred to in this article may not, and does not have to, have the exact correspondence to the physical operations of the process Based on the above recognition, a representative model can be built for each stage, using the conventional two-way PCA model This allows two-way PCA to be “directly” applied to a batch process after a proper stage division; a stage division algorithm is also developed in the article A three-tank process, as an experimental verification system, is finally introduced to illustrate the effectiveness of the proposed

Furong Gao

Papers

Review of Recent Research on Data-Based Process Monitoring

Survey on iterative learning control, repetitive control, and run-to-run control

Sub‐PCA modeling and on‐line monitoring strategy for batch processes

A fast estimation algorithm for lithium-ion battery state of health

A survey on multistage/multiphase statistical modeling methods for batch processes