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- Shanmugam, Kavitha, 1992-
(author)
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Circularity Assessment of Water and Waste in Cities : A Proposed Framework for Sustainable Performance Evaluation using LCA and LCC
- 2021
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Doctoral thesis (other academic/artistic)abstract
- Urbanization is a global phenomenon, happening on a massive scale and at a rapid rate, with 68% of the planet’s population predicted to be living in cities by 2050 (UN-DESA, 2018). The sustainability of a city (Goal 11 of UN SDGs) undergoing rapid urbanization depends on its ability to maintain a low consumption of resources and materials at any given time (referred to as the urban metabolic rate), whilst simultaneously providing essential municipal services to its inhabitants, such as a water supply, wastewater treatment and solid waste management. The latter must comply with circular economy principles, meaning recovery of byproducts, prevention of discharge of toxic pollutants, and avoidance of landfill usage. The appended papers in the thesis (Papers I–V) describe sustainable assessments of wastewater and waste services to increase their degree of circularity, using tools such as Life Cycle Assessment (LCA) and Life Cycle Costing (LCC). Paper I describes the environmental performance of using the biogas from a Wastewater Treatment Plant (WWTP) and converting it to Liquefied Biomethane (LBM), which can used as fuel in Tractor-Trailers (TT). Overall, the study suggests that changing from diesel to LBM fuel improves the environmental performance of TT. However, the magnitude of environmental benefit depends on an alternate source of electricity required for operation of the WWTP. Paper II evaluates the Social Cost-Benefit Analysis (SCBA) of Compressed Biomethane (CBM) obtained from a food waste digestion plant in Mumbai, India for use as a fuel in transit buses. SCBA results indicate that the food waste-based CBM model can save 6.86 billion Indian rupees (99.4 million USD) annually for Mumbai. Paper III describes the Sustainable Return on Investment (SROI) of lightweight Advanced High Strength Steel (AHSS) and Carbon Fiber Reinforced Polymer (CFRP) intensive multi-material Body in White (BIW) for automobiles. The SROI of CFRP BIWs is maximized when carbon fiber production uses energy from a low carbon-intensity electric grid or decentralized sources such as waste-to-energy incineration plants. Paper IV assesses the ecoefficiency of a thermal insulation panel that consists of a Polyurethane (PU) foam core sandwiched between two epoxy composite skins, prepared by reinforcing Glass Fibers (GF) and SFA (Silanized Fly Ash) in epoxy resin. The results revealed that the ecoefficiency of the composite panels is positive (47%) and superior to that of market incumbent alternatives with PU foam or rockwool cores and steel skins. Paper V quantifies the Total Cost to Society (TCS) (sum of private cost and environmental externalities cost) of a centralized urban WWTP, including the operation as well as byproduct utilization stream. The environmental performance and circular compliance are both factored in, when determining the TCS of a WWTP. The results revealed savings of 1.064 million USD, which include direct and indirect revenues to the plant, as well as avoidance costs attributed to environmental externalities. Based on the studies described in4these papers, a five-stage assessment framework for determining the overall sustainability performance of essential treatment services in a city is proposed in this thesis. The framework considers the combined effect of urban metabolic features and initiatives aimed at improving circular compliance of essential services.
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| 2. |
- Wang, Dong, 1987-
(author)
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How can data science contribute to a greener world? : an exploration featuring machine learning and data mining for environmental facilities and energy end users
- 2021
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Doctoral thesis (other academic/artistic)abstract
- Human society has taken many measures to address environmental issues. For example, deploying wastewater treatment plants (WWTPs) to alleviate water pollution and the shortage of usable water; using waste-to-energy (WtE) plants to recover energy from the waste and reduce its environmental impact. However, managing these facilities is taxing because the processes and operations are always complex and dynamic. These characteristics hinder the comprehensive and precise understanding of the processes through the conventional mechanistic models. On the other hand, with the development of the Fourth Industrial Revolution, large-volume and high-resolution data from automatic online monitoring have become increasingly obtainable. These data usually reflect abundant detailed information of process activities that can be utilized for optimizing process control. Similarly, data monitoring is also adopted by the resource end users. For example, energy consumption is usually recorded by commercial buildings for optimizing energy consumption behavior, eventually saving running costs and reducing carbon footprint. With the data recorded and retrieved, appropriate data science methods need to be employed to extract the desired information. Data science is a field incorporating formulating data-driven solutions, data preprocessing, analyzing data with particular algorithms, and employing results to support high-level decisions in various application scenarios.The aim of this PhD project is to explore how data science can contribute to a more sustainable world from the perspectives of both improving the operation of environmental engineering processes and optimizing the activities of energy end users. The major work and corresponding results are as follows:(1) (Paper I) An ML workflow consisting of Random Forest (RF) models, Deep Neural Network (DNN) models, Variable Importance Measure (VIM) analyses, and Partial Dependence Plot (PDP) analyses was developed and utilized to model WWTP processes and reveal how operational features impact on effluent quality. The case study was conducted on a full-scale WWTP in Sweden with large data (105,763 samples). This paper was the first ML application study investigating cause-and-effect relationships for full-scale WWTPs. Also, for the first time, time lags between process parameters were treated rigorously for accurate information uncovering. The cause-and-effect findings in this paper can contribute to more sophisticated process control that is more precise and cost-effective. (2) (Paper II) An upgraded workflow was designed to enhance the WWTP cause-and-effect investigation to be more precise, reliable, and comprehensive. Besides RF, two more typical tree-based models, XGBoost and LightGBM, were introduced. Also, two more metrics were adopted for a more comprehensive performance evaluation. A unified and more advanced interpretation method, SHapley Additive exPlanations (SHAP), was employed to aid model comparison and interpret the optimal models more profoundly. Along with the new local findings, this study delivered two significant general findings for cause-and-effect ML implementations in process industries. First, multi-perspective model comparison is vital for selecting a truly reliable model for interpretation. Second, adopting an accurate and granular interpretation method can profit both model comparison and interpretation.(3) (Paper III) A novel workflow was proposed to identify the accountable operational factors for boiler failures at WtE plants. In addition to data preprocessing and domain knowledge integration, it mainly comprised feature space embedding and unsupervised clustering. Two methods, PCA + K-means and Deep Embedding Clustering (DEC), were carried out and compared. The workflow succeeded in fulfilling the objective of a case study on three datasets from a WtE plant in Sweden, and DEC outperformed PCA + K-means for all the three datasets. DEC was superior due to its unique mechanism in which the embedding module and K-means are trained simultaneously and iteratively with the bidirectional information pass.(4) (Paper IV) A two-level (data structure level and algorithm mechanism level) workflow was put forward to detect imperceptible anomalies in energy consumption profiles of commercial buildings. The workflow achieved two objectives – it precisely detected the contextual energy anomalies hidden behind the time variation in the case study; it investigated the combined influence of data structures and algorithm mechanisms on unsupervised anomaly detection for building energy consumption. The overall conclusion was that the contextualization resulted in a less skewed estimation of correlations between instances, and the algorithms with more local perspectives benefited more from the contextualization.
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| 3. |
- Rebryk, Andriy, 1989-
(author)
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Comprehensive non-target screening to find and identify new biomagnifying organic contaminants in Baltic Sea top consumers
- 2022
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Doctoral thesis (other academic/artistic)abstract
- The development of industrial processes in the 19th and 20th centuries, in particular oil refining, resulted in a huge discovery and subsequent large-scale production of a variety of chemicals. These useful chemicals supposedly made the everyday lives of people easier and better by, for instance, controlling the spread of diseases such as malaria, through the use of DDT and other organochlorine pesticides (OCPs).During the 1970s and following decades, it was hypothesized and later shown, that these, and other “helpful chemicals” such as polychlorinated biphenyls (PCBs), played a crucial role in the steep population decline observed for multiple species in the Baltic Sea. They were classified as anthropogenic (man-made) hazardous substances (AHSs). Many AHSs can be stored in fatty tissues of the organisms and magnify in species at high trophic levels (predators) of the food web, as a result of persistence and transfer from lower-level organisms (prey). This process is called biomagnification and is characterized by biomagnification or trophic magnification factors (BMFs or TMFs, respectively). AHSs can be roughly divided into known chemicals of concern, such as persistent organic pollutants (POPs), and contaminants of emerging concern (CECs), that include novel flame retardants, polymer additives, and many more. Both the production and use of a number of AHSs have been regulated since the 1970s. To understand the outcome of the regulations, retrospective analysis of samples from different years, a time-trend study, is often utilized.The main aim of this work was to develop a non-selective sample extraction, purification, and analysis method, and then find and identify as many biomagnifying contaminants as possible. To assess both biomagnification and temporal trends of a wide range of chemical contaminants in a given Baltic Sea food web, non-target screening (NTS) was used. A clean-up method was established and tested with a satisfactory outcome: processed extracts were pure enough for gas chromatography-mass spectrometry (GC-MS) analysis. Also, accompanying NTS data processing workflows were developed. Application of these resulted in BMFs for more than 100 contaminants (Paper I). The data processing workflow was refined for faster detection of chemicals that demonstrate temporal trends and/or biomagnify. It was possible to detect and tentatively identify more than 300 legacy POPs and CECs with statistically significant temporal trends in three Baltic top consumers (Paper II). Adjusted NTS workflows were used to reveal more than 250 compounds that possessed trophic magnification properties (Paper III). Inspired by the discovery of a novel flame retardant Dechlorane 602 (Paper I), a suspect screening for dechlorane-related compounds and their transformation products was carried out. A total of 31 compounds were detected and tentatively identified, many of which showed significant temporal trends and biomagnification (Paper IV). A number of compounds reported in Papers I–IV were tentatively identified for the first time in wildlife. In addition, the papers provide valuable spectral and retention information for the researchers in the field.In conclusion, this thesis presents useful GC-MS-based NTS workflows and biomagnification or time-trend data for a plethora of organic contaminants in the Baltic Sea food web. The data can contribute to i) the assessment of the influence pollutants have on the ecosystem and ii) various mitigation actions for AHSs, such as evaluating dechloranes for regulation under the Stockholm Convention on POPs, helping in the fight for a better environment and future.
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| 4. |
- Mustafa, Majid, 1987-
(author)
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Removal of Micropollutants from Wastewater : evaluation of effect of upgrading ozonation to electro-peroxone
- 2020
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Doctoral thesis (other academic/artistic)abstract
- The United Nations (UN) has adopted 17 “Sustainable Development Goals” (SDGs) to achieve a clean, better and sustainable future. SDG 6 is to ensure that everyone has access to clean water and sanitation by 2030. According to the UN Educational, Scientific and Cultural Organization (UNESCO), more than 80% of wastewater produced from human actions is discharged into rivers or seas without any pollution removal. Thus, the presence of micropollutants (MPs: including, inter alia, pharmaceuticals, biocides and personal care products) in wastewaters is a major challenge that poses potential threats not only to aquatic system but also to humans due to their potential toxicity and potential to induce antibiotic resistance. Wastewater treatment plants (WWTPs) are considered hotspots for release of MPs as the current treatment processes are not designed to remove them. This thesis is based on studies described in four appended papers (Papers I-IV) designed to help efforts to solve these problems by investigating the factors involved and developing advanced treatment processes for removing MPs.Ozonation is one of the most intensively studied and widely used advanced treatment processes for removing MPs. However, due to ozone’s (O3) chemical selectivity, it cannot remove resistant MPs so its use (without additional treatments) results in their release into the environment. Thus, key objectives were to evaluate effects of switching to a new emerging process called electro-peroxone (E-peroxone) on MPs’ removal, by inserting two electrodes into an ozonation reactor. Its potential utility for other applications were also investigated.Paper I addresses effects of upgrading from ozonation to E-peroxone on pharmaceuticals’ removal at lab-scale, using a quantitative structure-activity relationship (QSAR) model. For this purpose, the relationship between QSAR model-predicted second-order rate constants of ozone’s reactions with pharmaceuticals (kO3 values) and ratios of experimentally determined pseudo-first order rate constants of E-peroxone and ozonation (kEP/kOZ values) was examined. Results showed that E-peroxone accelerated the removal of O3-resistant pharmaceuticals. In addition, the QSAR model predicted kO3 values for 491 pharmaceuticals, which suggested that large numbers of pharmaceuticals have high O3 resistance. Paper II addresses the removal of antimicrobials, including biocides and antibiotics, by E-peroxone and ozonation in relation to the water matrix. The results indicated that all studied antibiotics were effectively removed by both processes. In contrast, most of the biocides were at most moderately reactive with ozone, so their removal rate by ozonation was lower. The E-peroxone process increased their removal rate (i.e. removed them much more rapidly) by enhancing formation of hydroxyl radicals (•OH). Paper III reports the design, construction and tests of a pilot-scale mobile E-peroxone and ozonation system for removing naturally occurring MPs in secondary wastewater effluents. The tests included assessments of a new, scalable graphene modified carbon brush cathode for the E-peroxone process, which was found to enhance removal of moderately O3-reactive MPs significantly, and O3-resistant MPs moderately, while consuming similar amounts of electrical energy, or even less, for removing most of the MPs used in the experiments. Paper IV describes the regeneration of spent activated carbon, used for removing ionic MPs, by E-peroxone and ozonation. Both processes restored the activated carbon’s sorption efficiency to similar (or even higher) levels than that of virgin activated carbon, for all tested MPs except perfluorooctanoic acid (PFOA). It was concluded that sorption of MPs on regenerated activated carbon is mainly driven by interactions between ionic forms of the MPs with activated carbon’s charged surfaces rather than their interactions with pores in the activated carbon.
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