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The goal of this paper is to model the activated carbon adsorption capacity of cadmium using a polynomial regression model. The properties that influence the removal of heavy metals by activated carbon must be taken into consideration in order to synthesize materials specific to the characteristics of the pollutant.

Multivariable analysis applications allow a comprehensive description of the relationship between activated carbon surface properties and heavy metal adsorption.

The authors use a second-grade polynomial regression model to determine the influence of porosity and surface acidity in cadmium adsorption by activated carbon.

The authors propose a statistic analysis to correlate the carbon properties with its cadmium adsorption capacity. Model coefficient analysis will give a better comprehension of the influence of activated carbon porosity and surface acidity of cadmium removal.

The purpose of this paper is to model the corrosion rate behavior for two ferrous materials, carbon steel AISI 1020 and stainless steel AISI 304, immersed in ferric sulfate and ferric chloride solutions using D-optimal design with response surface methodology.

Experimental design addresses two factors (concentration and contact time) with multilevel categories, in order to predict and compare the corrosion rates of the studied materials immersed in flocculants solutions. A corrosion rate of specimens was calculated from mass loss determinations.

The authors used a polynomial model to fit the experimental values, thereby predicting significantly higher corrosion rates in ferric chloride solutions, as compared to ferric sulfate.

The authors propose a high fidelity model of the corrosion rate of each carbon steel and stainless steel material using D-optimal design with a response surface method (RSM).

Human resource allocation is considered a relevant problem in business process management (BPM). The successful allocation of available resources for the execution of process activities can impact on process performance, reduce costs and obtain a better productivity of the resources. In particular, process mining is an emerging discipline that allows improvement of the resource allocation based on the analysis of historical data. The purpose of this paper is to provide a broad review of primary studies published in the research area of human resource allocation in BPM and process mining.

A systematic mapping study (SMS) was conducted in order to classify the proposed approaches to allocate human resources. A total of 2,370 studies published between January 2005 and July 2016 were identified. Through a selection protocol, a group of 95 studies were selected.

Human resource allocation is an emerging research area that has been evolving over time, generating new proposals that are increasingly applied to real case studies. The majority of proposed approaches relate to the period 2011-2016. Journals and conference proceedings are the most common venues. Validation research and evaluation research are the most common research types. There are two main evaluation methods: simulation and case studies.

This study aims to provide an initial assessment of the state of the art in the research area of human resource allocation in BPM and process mining. To the best of the authors’ knowledge, this is the first research that has been conducted to date that generates a SMS in this research area.