Modelling gas-liquid mass transfer in wastewater treatment : when current knowledge needs to encounter engineering practice and vice versa

Abstract Gas–liquid mass transfer in wastewater treatment processes has received considerable attention over the last decades from both academia and industry. Indeed, improvements in modelling gas–liquid mass transfer can bring huge benefits in terms of reaction rates, plant energy expenditure, acid–base equilibria and greenhouse gas emissions. Despite these efforts, there is still no universally valid correlation between the design and operating parameters of a wastewater treatment plant and the gas–liquid mass transfer coefficients. That is why the current practice for oxygen mass transfer modelling is to apply overly simplified models, which come with multiple assumptions that are not valid for most applications. To deal with these complexities, correction factors were introduced over time. The most uncertain of them is the α-factor. To build fundamental gas–liquid mass transfer knowledge more advanced modelling paradigms have been applied more recently. Yet these come with a high level of complexity making them impractical for rapid process design and optimisation in an industrial setting. However, the knowledge gained from these more advanced models can help in improving the way the α-factor and thus gas–liquid mass transfer coefficient should be applied. That is why the presented work aims at clarifying the current state-of-the-art in gas–liquid mass transfer modelling of oxygen and other gases, but also to direct academic research efforts towards the needs of the industrial practitioners

Optimization methodology for high COD nutrient-limited wastewaters treatment using BAS process

Optimization of biofilm activated sludge (BAS) process via mathematical modelling is an entangle activity since economic, environmental objective and technical decision must be considered. This paper presents a methodology to optimize the operational conditions of BAS process in four steps by combining dynamic simulation techniques with non-linear optimization methods and with operative decision-making criteria. Two set of variables are separately prioritized in the methodology: essential variables related to physical operation to enforce established process performance, and refinement variables related to biological processes that can generate risks of bulking, pin-point floc and rising sludge. The proposed optimization strategy is applied for the treatment of high COD wastewater under nutrient limitation using an integrated mathematical model for COD removal that include predation, hydrolysis and a simplified approach to the limiting solids flux theory in the secondary clarifier in order to facilitate the convergence of the optimization solver. The methodology is implemented in a full-scale wastewater treatment plant for a cellulose and viscose fibre mill obtaining (i) improvement of the effluent quality index (Kg pollution/m3) up to 62% and, (ii) decrease the operating cost index (€/m3) of the process up to 30% respect the regular working operational conditions of the plant. The proposed procedure can be also applied to other biological treatments treating high COD nutrient-limited industrial wastewater such as from textile and winery production among others

Transforming data into knowledge for improved wastewater treatment operation : A critical review of techniques

The aim of this paper is to describe the state-of-the art computer-based techniques for data analysis to improve operation of wastewater treatment plants. A comprehensive review of peer-reviewed papers shows that European researchers have led academic computer-based method development during the last two decades. The most cited techniques are artificial neural networks, principal component analysis, fuzzy logic, clustering, independent component analysis and partial least squares regression. Even though there has been progress on techniques related to the development of environmental decision support systems, knowledge discovery and management, the research sector is still far from delivering systems that smoothly integrate several types of knowledge and different methods of reasoning. Several limitations that currently prevent the application of computer-based techniques in practice are highlighted

Validation of a decision support tool for wastewater treatment selection

Wastewater treatment selection is a complex task usually addressed by applying separate tools for the correct assessment of multi-criteria evaluation. Novedar_EDSS integrates technical, environmental, economic and social assessment capabilities in one single platform. The aim of this work is to evaluate and demonstrate the capabilities of this environmental decision support system (EDSS). For that purpose, 4 case studies of real projects were selected to validate the results in the EDSS by comparing them with those from the study of alternatives performed by the decision makers. Moreover, 1 conceptual case study was applied to support the selection of the most properly strategy for plant retrofitting. Results have demonstrated that the EDSS provides key aspects when deciding the retrofitting process to apply and, when compared to real projects, it recommends analogue treatments as those applied in the projects, ranking them in the same order. Therefore, results in the validation process performed show that this tool provides a reliable basis to support decision makers to select properly treatment alternatives in wastewater treatment plant pre-design

A knowledge-based decision support tool for selecting Eco wastewater treatment technologies in today\u2019s global complexities

A knowledge-based decision support tool for selecting Eco wastewater treatment technologies in today\u2019s global complexitie