Ensuring the reliable operation of wastewater transportation networks is critical for maintaining urban water infrastructure performance and preventing service disruptions. However, reliability assessment is typically conducted as a post-design evaluation rather than being explicitly integrated into the network synthesis and optimization process. This study proposes an integrated framework that combines Machine Learning (ML)-based reliability estimation with Graph-theory-based process optimization (P-graph) to support cost-effective infrastructure planning and retrofit decision-making. Extreme Gradient Boosting (XGBoost) and Neural Network models were developed to estimate pipeline failure rates using real data on physical, operational, and contextual characteristics of wastewater pipelines. The predicted failure rates were subsequently converted into reliability metrics and incorporated into a P-graph optimization framework to evaluate alternative retrofit configurations. The proposed methodology was demonstrated using a wastewater transportation network case study derived from the LeakDB benchmark dataset. The ML models effectively identified pipeline segments with elevated failure risk, providing valuable insights for asset management and maintenance prioritization. The optimization framework generated Pareto-optimal retrofit strategies that balance reliability enhancement and economic investment. Results indicate that the inclusion of distributed storage tanks and redundant pipelines substantially improves system reliability, achieving reliability levels exceeding 0.950 under full storage capacity. Sensitivity analysis further reveals that storage capacity is a critical determinant of system reliability, with significant reductions observed when the capacity falls below 50

Increased global plastic consumption and production boosted the amount of end-of-life (EoL) plastic. Also, 90 % of plastic EoL is either landfilled or incinerated. These unsustainable EoL pathways impact the environment and human health and waste valuable materials. Thus, improvements to the existing recycling infrastructure for sustainable plastic management are needed to enhance plastic circularity. Therefore, this contribution addresses optimizing cost-effective pathways for plastic recycling within the supply chain. The research uses mathematical optimization and the P-graph theoretical framework to calculate recycling costs, encompassing both capital expenditure and operational expenditure for various pathways of plastic recycling. The proposed methodology is applied through a detailed case study in Miskolc, Hungary, revealing estimated recycling costs ranging from 54.9 to 59.28 EUR/ton. This finding provides crucial insights into the economic implications of diverse recycling methods. Also, the study highlights the P-graph model's untapped potential as a resource for decision-makers in plastic recycling, particularly the enumeration of options for further consideration. The work's utility and novelty lie in the model's capability to design cost-effective pathways, offering a tangible contribution to the plastic recycling supply chain. Finally, this contribution offers economic solutions needed to ensure cost-effective sustainable plastic management solutions.

In this work, a modeling technique utilizing the P-Graph framework was used for a case study involving biomass-based local energy production. In recent years, distributed energy systems gained attention. These systems aim to satisfy energy supply demands, support the local economy, decrease transportation needs and dependence on imports, and, in general, obtain a more sustainable energy production process. Designing such systems is a challenge, for which novel optimization approaches were developed to help decision making. Previous work used the P-Graph framework to optimize energy production in a small rural area, involving manure, intercrops, grass, and corn silage as inputs and fermenters. Biogas is produced in fermenters, and Combined Heat and Power (CHP) plants provide heat and electricity. A more recent result introduced the concept of operations with flexible inputs in the P-Graph framework. In this work, the concept of flexible inputs was applied to model fermenters in the original case study. A new implementation of the original decision problem was made both as a Mixed-Integer Linear Programming (MILP) model and as a purely P-Graph model by using the flexible input technique. Both approaches provided the same optimal solution, with a 31% larger profit than the fixed input model.

We explored the application of Fisher information to the study of pandemics and illustrated the insights that can be gained using the COVID-19 pandemic, as a test case. To do so, we applied the Fisher information theory previously applied to periodic systems, to non-periodic dynamic systems. The resulting mathematical machinery was then used to compute the Fisher information measure, as the amount of information extracted from the time series for COVID-19 confirmed infections and deaths. The analysis was performed for the World as a whole and five nation-states: India, USA, Japan, Germany, and Chile. Several insights resulted from the study: (1) the information content of the time series varied widely for different time periods, over the course of the pandemic, (2) it is advisable not to fit model parameters or make policy decisions based on data from time periods with low Fisher information, (3) the most information about a wave of infections comes towards the end of the wave where the time series data has the most information about the dynamics of the pandemic, and (4) the quality of the time series data significantly affects the Fisher information value, and, therefore, what can be learned from studying the time series.

Considering the importance of water in the global Food-Energy-Water nexus, stress-dependent water pricing can be a valuable tool to achieve water sustainability. Given the large variability in water availability and demands across the globe, such mechanism should be implemented at regional scale. However, water pricing explicitly incorporating regional water stress has been rarely studied and used. Here, the generalized global sustainability model is modified and used to model continent-level stress-based water price and its effectiveness as a policy tool. The water price model includes a constant component representing the base price and a variable component which is a linear function of the water stress. The water stress feedback is modeled through the demand elasticity of water price. These models are parameterized for six global regions and three water-consuming sectors. Regional distribution of parameters is carried out based on GDP per capita, whereas sectoral distribution is obtained based on literature. The simulation results indicate that incorporating stress-based water price feedback reduces water stress for otherwise high water stress regions like Africa. Since the response to water price changes can reduce water stress, a water stress-based price model can be used as a policy instrument. This model can also capture the systemic progression of the influence of water price rise. The African continent may experience a reduction in food production by about 26

Inordinate consumption of natural resources by humans over the past century and unsustainable growth practices have necessitated a need for enforcing global policies to sustain the ecosystem and prevent irreversible changes. This study utilizes the Generalized Global Sustainability model (GGSM), which focuses on sustainability for the Food-Energy-Water (FEW) Nexus. GGSM is a 15-compartment model with components for the food web, microeconomic framework, energy, industry and water sectors, and humans. %It was validated based on historical data for global sectors and can predict population, global and regional water stress, GHG emissions, and the gross domestic product (GDP) for the next century. GGSM shows that an increased per capita consumption scenario is unsustainable. In this study, an optimal-control theory-based approach is devised to address the unsustainable scenario through policy interventions to evaluate sustainability by employing multiple global indicators and controlling them.

Biological control provides a sustainable alternative to chemical pesticides for controlling pests in agriculture. Chemical pesticides may lack the specificity to limit their adverse effects just on target species. However, the use of a biological control agent may cause ripple effects on ecological communities that exist within a farm or plantation. Ecological network analysis models can be used to anticipate such indirect impacts. In this work, we develop a graph theoretic model to gauge the effects of using a biological control agent to suppress the infestation of coconut plantations by scale insects. The model is based on the process graph technique described in our previous work. We retrospectively analyze the case of massive scale insect infestation of coconut plantations that occurred in the Philippines in the previous decade. Simulations with the model indicate the efficacy of biological control to suppress the infestation, particularly for serious outbreaks. On the other hand, use of a neonicotinoid poses undue collateral risks to the system because of its lethal effect on pollinators and on the biological control agents. Both of these results are corroborated by the actual field experience.

Crop shifting is considered as an important strategy to secure future food supply in the face of climate change. However, use of this adaptation strategy needs to consider the risk posed by changes in the geographic range of pests that feed on selected crops. Failure to account for this threat can lead to disastrous results. Models can be used to give insights on how best to manage these risks. In this paper, the socioecological process graph technique is used to develop a network model of interactions among crops, invasive pests, and biological control agents. The model is applied to a prospective analysis of the potential entry of the Colorado potato beetle into the Philippines just as efforts are being made to scale up potato cultivation as a food security measure. The modeling scenarios indicate the existence of alternative viable pest control strategies based on the use of biological control agents. Insights drawn from the model can be used as the basis to ecologically engineer agricultural systems that are resistant to pests.

Over the years, several global models have been proposed to forecast global sustainability, provide a framework for sustainable policy-making, or to study sustainability across the FEW nexus. An integrated model is presented here with components like food-web ecosystem dynamics, microeconomics components, including energy producers and industries, and various socio-techno-economic policy dimensions. The model consists of 15 compartments representing a simplified ecological food-web set in a macroeconomic framework along with a rudimentary legal system. The food-web is modeled by Lotka-Volterra type expressions, whereas the economy is represented by a price-setting model wherein firms and human households attempt to maximize their economic well-being. The model development is done using global-scale data for stocks and flows of food, energy, and water, which were used to parameterize this model. Appropriate proportions for some of the ecological compartments like herbivores and carnivores are used to model those compartments. The modeling of the human compartment was carried out using historical data for the global mortality rate. Historical data were used to parameterize the model. Data for key variables like the human population, GDP growth, greenhouse gases like CO2 and NOX emissions were used to validate the model. The model was then used to make long-term forecasts and to study global sustainability over an extended time. The purpose of this study was to create a global model which can provide techno-socio-economic policy solutions for global sustainability. Further, scenario analysis was conducted for cases where the human population or human consumption increases rapidly to observe the impact on the sustainability of the planet over the next century. The results indicated that the planet can support increased population if the per capita consumption levels do not rise. However, increased consumption resulted in exhaustion of natural resources and increased the CO2 emissions by a multiple of 100.

Co-culture systems can address food security issues by intensifying production of crops and animal protein without requiring additional land area. We show how a graph-theoretic optimization model based on ecological network analysis can determine resilient co-culture strategies by controlling the presence of key species. Results of simulations on a hybrid rice and crayfish production system indicate that comparable levels of productivity can be achieved with different ecological system structures.

Methodological tools such as Life Cycle Assessment (LCA), Exergy Analysis (ExA), and Emergy Analysis (EmA) that account for sustainability indicators in environmental, economic, and/or social dimensions, cannot provide an assessment under these three dimensions in a robust way by themselves. This research is proposing a sustainability assessment framework to obtain a unified performance metric (Integrated Sustainability Index, ISI) to assess the Triple Bottom Line – TBL. LCA, ExA, and EmA indicators are implemented in a complementary but not interchangeable manner, providing additional information for sustainability decision-making. The systematic approach is on a conceptual definition and calculation of sustainable environmental, social, and economic disaggregated indicators. These are then systematically combined into an Integrated Sustainability Index (ISI). EmA evaluates sustainability from a “donor-side” perspective, by assigning values to the environmental efforts and investment of nature to make and support flows, materials, and services; the system boundary is the geosphere. ExA evaluates sustainability through exergy efficiency under a “user-side” evaluation process (system boundary is the technosphere). LCA evaluates it based on the quantification of environmental impact by water, soil, and air emissions, caused by the use and processing of resources to provide products or services as a “user-side” method. The proposed sustainability index presents a comprehensible hierarchic structure supported by LCA, ExA, and EmA methodologies. The integration of social, environmental, and economic components into an index that also allows for the adjustment of externalities reducing the risk of subjectivity is a new approach to assessing sustainability.

Analysis of global sustainability is incomplete without an examination of the FEW nexus. Here, we modify the Generalized Global Sustainability Model (GGSM) to incorporate the global water system and project water stress on the global and regional levels. Five key water-consuming sectors considered here are agricultural, municipal, energy, industry, and livestock. The regions are created based on the continents, namely, Africa, Asia, Europe, North America, Oceania, and South America. The sectoral water use intensities and geographical distribution of the water demand were parameterized using historical data. A more realistic and novel indicator is proposed to assess the water situation: net water stress. It considers the water whose utility can be harvested, within economic and technological considerations, rather than the total renewable water resources. Simulation results indicate that overall global water availability is adequate to support the rising water demand in the next century. However, regional heterogeneity of water availability leads to high water stress in Africa. Africa's maximum net water stress is 140%, so the water demand is expected to be more than total exploitable water resources. Africa might soon cross the 100% threshold/breakeven in 2022. For a population explosion scenario, the intensity of the water crisis for Africa and Asia is expected to rise further, and the maximum net water stress would reach 149% and 97%, respectively. The water use efficiency improvement for the agricultural sector, which reduces the water demand by 30%, could help to delay this crisis significantly.

Inordinate consumption of natural resources by humans over the past century and unsustainable growth practices have necessitated a need for enforcing global policies to sustain the ecosystem and prevent irreversible changes. This study utilizes the Generalized Global Sustainability model (GGSM), which focuses on sustainability for the Food-Energy-Water (FEW) Nexus. GGSM is a 15-compartment model with components for the food-web, microeconomic framework, energy, industry and water sectors, and humans. GGSM shows that an increased per capita consumption scenario is unsustainable. In this study, an optimal-control theory based approach is devised to address the unsustainable scenario through policy interventions to evaluate sustainability by employing multiple global indicators and controlling them. Six policy options are employed as control variables to provide global policy recommendations to develop the multi-variate optimal control approach. Seven objectives are proposed to limit the human burden on the environment to ascertain sustainability from a lens of ecological, economic, and social wellbeing. This study observes the performance of the policy options toward seven sustainability indicators: Fisher Information, Green Net Product, Ecological Buffer, Carbon dioxide emissions, Nitrous oxide emissions, and Global Water Stress. The optimal control model assesses these multiple objectives by minimizing the variance in the Fisher Information. One significant result from this study is that optimizing for the Fisher Information based objective is adequate to attain sustainability and manage the other objectives under consideration. Thus, forgoing a multi-objective problem framework. The results show that cross-dimensional policy interventions such as increased vegetarianism and increased penalty on industrial discharge are shown to have a positive impact on scale.

Designing effective wastewater treatment networks is challenging because of the large number of treatment options available for performing similar tasks. Each treatment option has variability in cost and contaminant removal efficiency. Moreover, their mathematical models are highly nonlinear, thus rendering them computa-tionally intensive. Such systems yield mixed-integer nonlinear programming models which cannot be solved properly with contemporary optimization tools that may result in local optima or may fail to converge. Herein, the P-graph framework is employed, thus generating all potentially feasible process structures, which results in simpler, smaller mathematical models. All potentially feasible process networks are evaluated by nonlinear programming resulting in guaranteed global optimum; furthermore, the ranked list of the n-best networks is also available. With the proposed tool, better facilities can be designed handling complex waste streams with minimal cost and reasonable environmental impact. The novel method is illustrated with two case studies showing its computational effectiveness.