Papers by Mark Gino K . Galang, PhD
Chemosensors, 2021
Odour emissions are a global issue that needs to be controlled to prevent negative impacts. Instr... more Odour emissions are a global issue that needs to be controlled to prevent negative impacts. Instrumental odour monitoring systems (IOMS) are an intelligent technology that can be applied to continuously assess annoyance and thus avoid complaints. However, gaps to be improved in terms of accuracy in deciphering information, especially in the implementation of the mathematical model, are still being researched, especially in environmental odour monitoring applications. This research presents and discusses the implementation of traditional and innovative parametric and non-parametric prediction techniques for the elaboration of an effective odour quantification monitoring model (OQMM), with the aim of optimizing the accuracy of the measurements. Artificial neural network (ANN), multivariate adaptive regression splines (MARSpline), partial least square (PLS), multiple linear regression (MLR) and response surface regression (RSR) are implemented and compared for prediction of odour conce...
Journal of Water Process Engineering, 2024
Case Studies in Chemical and Environmental Engineering 7 (2023) 100348, 2023
The cultivation of microalgae for carbon capture and utilization (CCU) emerged as sustainable and... more The cultivation of microalgae for carbon capture and utilization (CCU) emerged as sustainable and effective platform to reduce GHG S and produce valuable biomass. In the study, systematic comparison of two identical algal photo-bioreactors (PBRw and PBRp), with white and purple led lights respectively, has been implemented. Carbon removals up to 98% has been obtained, with PBRp supporting enhanced cultivation conditions, higher CO 2 removals and increased biomass production (up to 855 mg d − 1 of dry algal biomass). The results demonstrate the potential of the proposed solutions as sustainable strategy to increase the applicability of algal photobioreactors for carbon capture and utilization.
Chemosphere 303 (2022) 136665, 2022
Chemical Engineering Transactions, 2022
Chemical Engineering Transactions, 2022
Chemosensors, 2021
Odour emissions are a global issue that needs to be controlled to prevent negative impacts. Instr... more Odour emissions are a global issue that needs to be controlled to prevent negative impacts. Instrumental odour monitoring systems (IOMS) are an intelligent technology that can be applied to continuously assess annoyance and thus avoid complaints. However, gaps to be improved in terms of accuracy in deciphering information, especially in the implementation of the mathematical model, are still being researched, especially in environmental odour monitoring applications. This research presents and discusses the implementation of traditional and innovative parametric and non-parametric prediction techniques for the elaboration of an effective odour quantification monitoring model (OQMM), with the aim of optimizing the accuracy of the measurements. Artificial neural network (ANN), multivariate adaptive regression splines (MARSpline), partial least square (PLS), multiple linear regression (MLR) and response surface regression (RSR) are implemented and compared for prediction of odour concentrations using an advanced IOMS. Experimental analyses are carried out by using real environmental odour samples collected from a municipal solid waste treatment plant. Results highlight the strengths and weaknesses of the analysed models and their accuracy in terms of environmental odour concentration prediction. The ANN application allows us to obtain the most accurate results among the investigated techniques. This paper provides useful information to select the appropriate computational tool to process the signals from sensors, in order to improve the reliability and stability of the measurements and create a robust prediction model.
Water
The release of air pollutants from the operation of wastewater treatment plants (WWTPs) is often ... more The release of air pollutants from the operation of wastewater treatment plants (WWTPs) is often a cause of odor annoyance for the people living in the surrounding area. Odors have been indeed recently classified as atmospheric pollutants and are the main cause of complaints to local authorities. In this context, the implementation of effective treatment solutions is of key importance for urban water cycle management. This work presents a critical review of the state of the art of odor treatment technologies (OTTs) applied in full-scale WWTPs to address this issue. An overview of these technologies is given by discussing their strengths and weaknesses. A sensitivity analysis is presented, by considering land requirements, operational parameters and efficiencies, based on data of full-scale applications. The investment and operating costs have been reviewed with reference to the different OTTs. Biofilters and biotrickling filters represent the two most applied technologies for odor a...
Chemosphere, 2021
Odour emissions from complex industrial plants may cause potential impacts on the surrounding are... more Odour emissions from complex industrial plants may cause potential impacts on the surrounding areas. Consequently, the validation of effective tools for the control of the associated environmental pressures, without hindering economic growth, is strongly needed. Nowadays, senso-instrumental methods by using Instrumental Odour Emissions Systems (IOMSs) is among the most attractive tool for the continuous monitoring of environmental odours, allowing the possibility of obtaining real-time information to support the decision-making process and proactive approach. The systems complexity and scarcity of real data limited their wider full-scale employment. The study presents an advanced prototype of IOMS for the continuous classification and quantification of the odours emitted in ambient air by complex industrial plants, to continuously control the plants emissions with backwards approach. The IOMS device was designed and optimized and included the system for the automatic control of the conditions inside the measurement chamber. The designed operational procedures were presented and discussed. Results highlighted the influence of temperature and air flow rate for the measurement repeatability. Accurate prediction model was created and optimized and resulted able to distinguish 3 different industrial odour sources with accuracy approximately equal to 96%. The models were optimized thanks to the software features, which allowed to automatically apply the designed statistical procedures on the identified dataset with different pre-processing approach. The usefulness of having a fully-developed and user-friendly flexible system that allowed to select and automatically compare different settings options, including the different feature extraction methods, was demonstrated in order to identify the best prediction model.
Environment International, 2019
Chemical Engineering Transactions, 2018
Global NEST Journal, 2018
Waste mobile phone is one of the subgroups of e-waste which is defined as discarded electronic pr... more Waste mobile phone is one of the subgroups of e-waste which is defined as discarded electronic products in the Philippine context. This study estimated current and projected quantities of waste mobile phones in the country using feed forward neural network. The neural network architecture used had three layers: (i) input layer, (ii) hidden layer, and (iii) output layer. Seven input factors were fed to the network: (i) population, (ii) literacy rate, (iii) mobile connections, (iv) mobile subscribers, (v) gross domestic product (GDP), (vi) GDP per capita, and (vii) US dollar to Philippine peso exchange rate. These input factors were selected based on the criteria provided in the study by the Groupe Spéciale Mobile Association (GSMA) Intelligence in 2015 on why the Philippines is an innovation hub in mobile industry and the availability of data from the sources. The structure was designed with five hidden layers which consisted of (i) six neurons for layer 1, (ii) five neurons for layer 2, (iii) four neurons for layer 3, (iv) three neurons for layer 4, and (v) two neurons for layer 5. The neural network was designed to initially calculate the sales of mobile phones before estimating waste mobile phone generation. Visual Gene Developer 1.7 Software was used which achieved a sum of squared error of 0.00001. Estimated values were found to be in good agreement with a calculated accuracy of 99%. This study can be used by policy makers as basis for strategy formulation and as guideline and baseline data for establishing a proper management system. Neural network performed better than the traditional linear extrapolation method for forecasting of data.
conferences by Mark Gino K . Galang, PhD
CEST 2021, 2021
Air quality protection and control is an issue of growing interest. The aspects related to the sp... more Air quality protection and control is an issue of growing interest. The aspects related to the spread of t h e coronavirus have accentuated this attention. Furthermore, among the emerging contaminants (EC's) in ambient a ir, the microplastics (1-5 μm) are a great concern a risin g from anthropogenic activities. These pollutants may bring detrimental effects on human health. To control the EC's, the first activity is the characterization. To date, lim it ed studies highlight and describe technologies able to identify and measure the presence in the air of these types of emerging pollutants (EP's). Furthermore, the presented studies show a methodology gap in their experiments. The research presents and discusses the state-of-t heart adopted technologies to characterize MPs in ambient air and pointing out strengths and weaknesses. Kn o wledge gap, uncertainties and recommendations are highligh t ed. The paper provides useful information in enhanced monitoring to support policymakers in emerging microplastics pollutants and related issues, as well as potential smart technology to be implemented.
16th International Conference on Environmental Science and Technology, 2019
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Papers by Mark Gino K . Galang, PhD
conferences by Mark Gino K . Galang, PhD