Energy rating of a water pumping station using multivariate analysis

Among water management policies, the preservation and the saving of energy demand in water supply and treatment systems play key roles. When focusing on energy, the customary metric to determine the performance of water supply systems is linked to the definition of component-based energy indicators. This approach is unfit to account for interactions occurring among system elements or between the system and its environment. On the other hand, the development of information technology has led to the availability of increasing large amount of data, typically gathered from distributed sensor networks in so-called smart grids. In this context, data intensive methodologies address the possibility of using complex network modeling approaches, and advocate the issues related to the interpretation and analysis of large amount of data produced by smart sensor networks. In this perspective, the present work aims to use data intensive techniques in the energy analysis of a water management network. The purpose is to provide new metrics for the energy rating of the system and to be able to provide insights into the dynamics of its operations. The study applies neural network as a tool to predict energy demand, when using flowrate and vibration data as predictor variables

On the role of pre and post-processing in environmental data mining

The quality of discovered knowledge is highly depending on data quality. Unfortunately real data use to contain noise, uncertainty, errors, redundancies or even irrelevant information. The more complex is the reality to be analyzed, the higher the risk of getting low quality data. Knowledge Discovery from Databases (KDD) offers a global framework to prepare data in the right form to perform correct analyses. On the other hand, the quality of decisions taken upon KDD results, depend not only on the quality of the results themselves, but on the capacity of the system to communicate those results in an understandable form. Environmental systems are particularly complex and environmental users particularly require clarity in their results. In this paper some details about how this can be achieved are provided. The role of the pre and post processing in the whole process of Knowledge Discovery in environmental systems is discussed

Evaluation of engineered nanoparticle toxic effect on wastewater microorganisms: current status and challenges

The use of engineered nanoparticles (ENPs) in a wide range of products is associated with an increased concern for environmental safety due to their potential toxicological and adverse effects. ENPs exert antimicrobial properties through different mechanisms such as the formation of reactive oxygen species, disruption of physiological and metabolic processes. Although there are little empirical evidences on environmental fate and transport of ENPs, biosolids in wastewater most likely would be a sink for ENPs. However, there are still many uncertainties in relation to ENPs impact on the biological processes during wastewater treatment. This review provides an overview of the available data on the plausible effects of ENPs on AS and AD processes, two key biologically relevant environments for understanding ENPs–microbial interactions. It indicates that the impact of ENPs is not fully understood and few evidences suggest that ENPs could augment microbial-mediated processes such as AS and AD. Further to this, wastewater components can enhance or attenuate ENPs effects. Meanwhile it is still difficult to determine effective doses and establish toxicological guidelines, which is in part due to variable wastewater composition and inadequacy of current analytical procedures. Challenges associated with toxicity evaluation and data interpretation highlight areas in need for further research studies

The active microbial community more accurately reflects the anaerobic digestion process: 16S rRNA (gene) sequencing as a predictive tool

Background: Amplicon sequencing methods targeting the 16S rRNA gene have been used extensively to investigate microbial community composition and dynamics in anaerobic digestion. These methods successfully characterize amplicons but do not distinguish micro-organisms that are actually responsible for the process. In this research, the archaeal and bacterial community of 48 full-scale anaerobic digestion plants were evaluated on DNA (total community) and RNA (active community) level via 16S rRNA (gene) amplicon sequencing. Results: A significantly higher diversity on DNA compared with the RNA level was observed for archaea, but not for bacteria. Beta diversity analysis showed a significant difference in community composition between the DNA and RNA of both bacteria and archaea. This related with 25.5 and 42.3% of total OTUs for bacteria and archaea, respectively, that showed a significant difference in their DNA and RNA profiles. Similar operational parameters affected the bacterial and archaeal community, yet the differentiating effect between DNA and RNA was much stronger for archaea. Co-occurrence networks and functional prediction profiling confirmed the clear differentiation between DNA and RNA profiles. Conclusions: In conclusion, a clear difference in active (RNA) and total (DNA) community profiles was observed, implying the need for a combined approach to estimate community stability in anaerobic digestion

Data mining as a tool for environmental scientists

Over recent years a huge library of data mining algorithms has been developed to tackle a variety of problems in fields such as medical imaging and network traffic analysis. Many of these techniques are far more flexible than more classical modelling approaches and could be usefully applied to data-rich environmental problems. Certain techniques such as Artificial Neural Networks, Clustering, Case-Based Reasoning and more recently Bayesian Decision Networks have found application in environmental modelling while other methods, for example classification and association rule extraction, have not yet been taken up on any wide scale. We propose that these and other data mining techniques could be usefully applied to difficult problems in the field. This paper introduces several data mining concepts and briefly discusses their application to environmental modelling, where data may be sparse, incomplete, or heterogenous

A Novel Ensemble Adaptive Sparse Bayesian Transfer Learning Machine for Nonlinear Large-Scale Process Monitoring.

Process monitoring plays an important role in ensuring the safety and stable operation of equipment in a large-scale process. This paper proposes a novel data-driven process monitoring framework, termed the ensemble adaptive sparse Bayesian transfer learning machine (EAdspB-TLM), for nonlinear fault diagnosis. The proposed framework has the following advantages: Firstly, the probabilistic relevance vector machine (PrRVM) under Bayesian framework is re-derived so that it can be used to forecast the plant operating conditions. Secondly, we extend the PrRVM method and assimilate transfer learning into the sparse Bayesian learning framework to provide it with the transferring ability. Thirdly, the source domain (SD) data are re-enabled to alleviate the issue of insufficient training data. Finally, the proposed EAdspB-TLM framework was effectively applied to monitor a real wastewater treatment process (WWTP) and a Tennessee Eastman chemical process (TECP). The results further demonstrate that the proposed method is feasible

Development of a Conceptual, Mathematical and Model of System Dynamics for Landfill Water Treatment

Leachate is a major problem in landfills due to the type and amount of pollutants. In Croatia, the usual way of handling leachate is recirculation back to the landfill body. However, this method poses a danger of their leakage into the environment, especially during periods of increased precipitation. Leachate is heavily polluted with organic matter, and its spillage into the environment can cause environmental incident. This paper presents a model for efficient treatment of landfill water contaminated with organic matter, based on the operating parameters of the actual water treatment system. The aim of this scientific research is to develop a model for landfill water treatment and to design a methodology suitable for significant patterns of organic matter pollution behaviour. The developed conceptual model is a computer-based model that uses randomly selected values from the theoretical probability distribution of the applied variables. The mathematical model is based on a system of differential equations solved by the Runge-Kutta method. To validate the model, a nonparametric test was applied, given that the distributions are asymmetric non-Gaussian distributions. The methodology proposed in this paper is based on simulation modelling as a useful method in environmental protection. The developed and validated model has proven that landfill water can be effectively and economically purified. Simulation modelling and environmental informatics can effectively contribute to solving environmental problems on the computer without unnecessary risk to the environment

A review of the use of artificial intelligence methods in infrastructure systems

The artificial intelligence (AI) revolution offers significant opportunities to capitalise on the growth of digitalisation and has the potential to enable the ‘system of systems’ approach required in increasingly complex infrastructure systems. This paper reviews the extent to which research in economic infrastructure sectors has engaged with fields of AI, to investigate the specific AI methods chosen and the purposes to which they have been applied both within and across sectors. Machine learning is found to dominate the research in this field, with methods such as artificial neural networks, support vector machines, and random forests among the most popular. The automated reasoning technique of fuzzy logic has also seen widespread use, due to its ability to incorporate uncertainties in input variables. Across the infrastructure sectors of energy, water and wastewater, transport, and telecommunications, the main purposes to which AI has been applied are network provision, forecasting, routing, maintenance and security, and network quality management. The data-driven nature of AI offers significant flexibility, and work has been conducted across a range of network sizes and at different temporal and geographic scales. However, there remains a lack of integration of planning and policy concerns, such as stakeholder engagement and quantitative feasibility assessment, and the majority of research focuses on a specific type of infrastructure, with an absence of work beyond individual economic sectors. To enable solutions to be implemented into real-world infrastructure systems, research will need to move away from a siloed perspective and adopt a more interdisciplinary perspective that considers the increasing interconnectedness of these systems