This study presents an innovative approach to risk-aware decision-making in water resource management. We focus on a case study in the Netherlands, where risk awareness is key to water system design and policy-making. Recognizing the limitations of deterministic methods in the face of weather, energy system, and market uncertainties, we propose a scalable stochastic Model Predictive Control (MPC) framework that integrates probabilistic forecasting, scenario generation, and stochastic optimal control. We utilize Combined Quantile Regression Deep Neural Networks and Non-parametric Bayesian Networks to generate probabilistic scenarios that capture realistic temporal dependencies. The energy distance metric is applied to optimize scenario selection and generate scenario trees, ensuring computational feasibility without compromising decision quality. A key feature of our approach is the introduction of Exceedance Risk (ER) constraints, inspired by Conditional-Value-at-Risk (CVaR), to enable more nuanced and risk-aware decision-making while maintaining computational efficiency. In this work, we enable the Noordzeekanaal–Amsterdam-Rijnkanaal (NZK-ARK) system to participate in Demand Response (DR) services by dynamically scheduling pumps to align with low hourly electricity prices on the Day Ahead and Intraday markets. Through historical simulations using real water system and electricity price data, we demonstrate that incorporating uncertainty can significantly reduce operational costs—by up to 44 percentage points compared to a deterministic approach—while maintaining safe water levels. The modular nature of the framework also makes it adaptable to a wide range of applications, including hydropower and battery storage systems.

Effective reservoir flood control demands real-time decision-making that balances multiple objectives. However, traditional optimization approaches are often too computationally intensive and become intractable when considering dynamically changing preferences of operators, modelled as weights of different objectives. This study aims to develop tractable real-time flood control strategies that maintain performance while reducing computational complexity. We propose two data-driven approaches based on Model Predictive Control (MPC): (1) an explicit MPC using deep neural networks to directly determine optimal outflow schedules, and (2) a switched MPC that produces optimal weights of objectives based on hydrological conditions. Both methods leverage offline learning from an online Parameterized Dynamic MPC framework incorporating state-dependent weights. We tested these approaches on South Korea’s Daecheong multipurpose reservoir using historical flood events with various patterns. The explicit MPC demonstrated reliable performance under conditions similar to its training data. However, it showed frequent changes in outflow schedules and constraint violations for scenarios outside training data. In contrast, the switched MPC maintained robustness across all test scenarios due to a linear optimization process in a receding horizon manner, though with slightly reduced performance compared to the explicit MPC under scenarios inside the range of training data. Most significantly, both approaches reduced computation time from approximately 10 minutes to less than one second, making real-time implementation feasible. This dramatic improvement enables prompt decision-making during rapidly evolving flood events while maintaining near-optimal control performance.

This study addresses the pressing need for sustainable energy infrastructure in the Eastern Nile Basin region, focusing on the integration of Floating Solar Photovoltaics (FPVs) in long-term energy planning. FPVs offer advantages over land-based photovoltaics, such as reducing capital costs by utilizing existing infrastructure at hydropower dams and reducing evaporation. Given the region's growing population and high competition for water, our research introduces a novel framework that explores the dual benefits of water conservation and reduced land use, alongside policy targets for lowering carbon emissions through increased integration of renewables in the power mix.

The study advances existing models by incorporating FPV technology into the OSeMOSYS tool, an open-source model for optimizing national energy generation mixes. Our research presents a spatially explicit framework for long-term energy system planning that integrates land use and water conservation metrics at reservoirs within the energy planning process. The role of FPVs in the region’s energy pathways is evaluated by endogenizing the costs of CO2 emissions and land use, while considering water savings. Our analysis develops and implements a new methodology for land-use accounting and pricing, and assesses the potential of FPVs to reduce evaporation across a network of hydropower reservoirs. This expanded modeling framework is then utilized to analyze various scenarios, including different hydrological regimes under CMIP climate change projections and policy measures such as the introduction of taxes on carbon emissions and land-use, and regional electricity trade links.

Results indicate that FPVs can cost-effectively provide up to 3% of the region's electricity generation by 2065, saving up to 376 million cubic meters of water annually. Scenarios introducing carbon and land-use taxes increase FPV's share in the power generation mix to 4.5% and enable earlier FPV deployment. While climate impacts minimally affect FPV's role, the technology slightly reduces CO2 emissions (0.4%) and land use (1.6%) in the baseline scenario without taxes. A carbon tax alone reduces emissions by 11-23% but raises land use by up to 8% due to increased wind, hydro, and solar deployment. Land tax alone reduces land use by 5-8% with minimal impact on emissions. However, combining land and carbon taxes reduces emissions (by 12% to 22%) and land use (a decrease of 1.6% or an increase of 1.2%). The optimal locations for FPV deployment are identified as Lake Nasser (2.1 GW), Renaissance Dam (6.4 GW), and Merowe Dam (1.2 GW), leveraging existing hydropower infrastructure. These findings demonstrate that FPVs represent a promising adaptation strategy for energy planning offering multiple co-benefits including reduced water evaporation, efficient land use, and emissions mitigation, particularly when supported by appropriate environmental pricing policies.

Temperature plays a critical role in performance and stability of anaerobic digestion processes, subject to frequent meteorological fluctuations. However, state-of-the-art modeling and process control approaches for anaerobic digestion often do not consider the temporal dynamics of the temperature, which can influence microbial communities, kinetics, and chemical equilibrium, and consequently, biogas production efficiency. Therefore, to account for anaerobic digesters operating under fluctuating meteorological conditions, the Anaerobic Digestion Model no. 1 (ADM1) is mechanistically extended in this paper to incorporate temporal changes into temperature-dependent parameters by defining inhibition functions for microbial activities using the cardinal temperature model, and accounting for the lag in microbial adaptation to temperature fluctuations using a time-lag adaptation function. Thereafter, given that temperature fluctuations are a significant disturbance, a control framework based on Model Predictive Control (MPC) is developed to regulate the feeding flow rate and to ensure stable production rates despite temperature disturbances without relying on direct temperature control. An adaptive MPC approach is formulated based on a linear input–output model, where the parameters of the linear model are updated online to capture the nonlinear dynamics of the process and frequent changes in the dynamics accurately. In addition, a fuzzy logic system is employed to assign a reference trajectory for the production rate based on the temperature and its rate of change. Integrating this fuzzy logic system with the MPC controller enhances the production rate on warm days and avoids the operational failure in production on cold days. Additionally, to enhance biogas production rates, the feasibility of utilizing a portion of the produced biogas for external heating purposes is also investigated. It is demonstrated that by utilizing the proposed MPC approach, the additional amount of feed for the digester to produce methane required for a self-consumption biogas-fueled heating system can be calculated according to the meteorological variations. This enhances the process performance and stability. Finally, a thermally optimized dome digester semi-buried in the ground, operating under climate conditions of The Netherlands is considered as a case study to validate the extended model in agreement with biological and physicochemical behaviors of real-world applications, and to demonstrate the effectiveness of the proposed control system in handling temperature changes and enhancing performance.

Optimising rainwater harvesting (RWH) systems’ design involves sizing the storage and catchment areas to enhance cost-effectiveness, self-sufficiency, and water quality indicators. This paper considers the design of RWH systems under long-term uncertainty in precipitation and demands. In this work, we formulate and solve a multi-objective stochastic optimisation problem that allows explicit trade-offs under uncertainty, maximising system efficiency and minimising deployment cost. We use the yield after spillage (YAS) approach to incorporate the physical and operational constraints and the big-M method to reformulate the nonlinear min\max rules of this approach as a mixed-integer linear programming (MILP) problem. By posing a risk averseness measure on efficiency as a conditional value at risk (CVaR) formulation, we guarantee the designer against the highest demand and driest weather conditions. We then exploit the lexicographic method to effectively solve the multi-objective stochastic problem as a sequence of equivalent single-objective problems. A detailed case study of a botanical garden in Amsterdam demonstrates the framework's practical application; we show significant improvements in system efficiency of up to 15.5% and 28.9% in the driest scenarios under risk-neutral and risk-averse conditions, respectively, compared to deterministic approaches. The findings highlight the importance of taking into account multiple objectives and uncertainties when designing RWH systems, allowing designers to optimise efficiency and costs based on their specific requirements without extensive parameterisation.

Model predictive control (MPC) is an optimal control strategy suited for flood control of water resources infrastructure. Despite many studies on reservoir flood control and their theoretical contribution, optimisation methodologies have not been widely applied in real-time operation due to disparities between research assumptions and practical requirements. To address this gap, we include practical objectives, such as minimising the magnitude and frequency of changes in the existing outflow schedule. Incorporating these objectives transforms the problem into a multi-objective nonlinear optimisation problem that is difficult to solve in real time. Additionally, it is reasonable to assume that the weights and some parameters, considered the operators’ preferences, vary depending on the system state. To overcome these limitations, we propose a framework that converts the original intractable problem into parameterised linear MPC problems with dynamic optimisation of weights and parameters. This is done by introducing a model-based learning concept. We refer to this framework as Parameterised Dynamic MPC (PD-MPC). The effectiveness of this framework is demonstrated through a numerical experiment for the Daecheong multipurpose reservoir in South Korea. We find that PD-MPC outperforms standard MPC-based designs without a dynamic optimisation process for the objective weights and model parameter. Moreover, we demonstrate that the weights and parameters vary with changing hydrological conditions.

Aquatic ecosystems provide valuable ecosystem services (e.g., habitat for fisheries) to surrounding communities but environmental degradation can diminish the quality of these ecosystem services. The Lake Tana basin, including the Gumara River and its associated wetlands in Ethiopia, has experienced rapid environmental change in the last several decades. Changes in the export of nutrients from the uplands might contribute to the rapid degradation of aquatic ecosystem services due to the expansion of water hyacinths and declines in fish biodiversity and yields. We estimate how human modification and climate change have impacted watershed nutrient retention from 1986 to 2020. Here we (1) examine trends in surface water chemistry, watershed land use/land cover change, and flow alterations; (2) estimate the watershed nutrient delivery ratio (NDR), a metric of watershed nutrient retention, through time; and (3) examine how fishery yields and water hyacinth infestation in Lake Tana at the outlet of the Gumara River change during a period of rapid increase in nutrient export from the Gumara River. Estimates of the surface load and export of both phosphorus (P) and nitrogen (N) from the Gumara River watershed were approximately stable between 1986 and 2009, but from 2014 to 2020 exports increased by 69 % for P and 80 % for N. Potential factors driving this rapid increase include an expansion in irrigation for agriculture, land conversion to eucalyptus plantations, decreases in dry season flow, and an increase in mean annual precipitation since 2009. In addition, the increase in nutrient export from the Gumara River watershed coincides with a near extirpation of fish in the Gumara River and a ten-fold expansion of water hyacinth downstream in Lake Tana. Human activity and hydrological alteration in the Gumara River watershed have resulted in water quality changes, declines in fish populations, and the expansion of invasive species. Long-term monitoring and watershed modeling can help inform the management of regionally important aquatic ecosystems such as the Gumara River and Lake Tana.

Africa's abundant renewable energy resources and vast land areas present an unprecedented opportunity for the development of a green hydrogen economy. Several countries in Africa have already initiated ambitious projects to establish green hydrogen economies, mainly targeting export markets in Europe. This perspective article explores the potential of green hydrogen in Africa and examines the challenges and risks associated with green hydrogen development on the continent. We believe that the role of hydrogen in the transformation of Africa should go beyond energy export and aim to harness its competitive advantage for various industries that require low-cost green hydrogen. We argue that prevailing extractive and geopolitical realities pose a major barrier in Africa’s path towards harnessing the full potential of green hydrogen for its development. To break this cycle, Africa must demand equitable partnerships, invest in technology transfer, and foster collaboration to drive a transformative green hydrogen revolution that is aligned with Agenda 2063s vision for a self-empowered and sustainable Africa.

Key policy insights:

- Green-hydrogen has the potential to provide affordable energy to transport and industry, accelerating sustainable development in Africa.

- The current resource development practice in Africa will constrain the continent’s ability to chart an independent course towards a sustainable green hydrogen economy.

- There is a need for clear regulation, incentives, and financial support for the adoption of green hydrogen technology.

- It is important that national governments create local demand which can stimulate new and sustainable jobs, and bilateral and regional collaborations to build and operate hydrogen infrastructure.

- Despite the high technical potential, Africa’s role in the global hydrogen market is hindered by limitation in access to finance, technology, infrastructure, and policy stability.

- Africa must assert itself on the global stage, demanding equitable partnerships and investments that foster technological transfer and knowledge sharing that benefit wider socio-economic development.

This white paper aims to provide an introduction to the topic of Design for Justice for a wide audience. It demonstrates ongoing research on this topic by the TU Delft community and contributes to the exchange of relevant knowledge and expertise, as one of the outcomes of the activities organised for the Delft Design for Values Institute’s annual theme ‘Design for Justice’. This document includes recommendations on how to foster Design for Justice, which are not just relevant for designers, engineers, and academic researchers, but also for educators and policy makers.

Purple Phototrophic Bacteria (PPB) are increasingly being applied in resource recovery from wastewater. Open raceway-pond reactors offer a more cost-effective option, but subject to biological and environmental perturbations. This study proposes a hierarchical control system based on Adaptive Generalized Model Predictive Control (AGMPC) for PPB raceway reactors. The AGMPC uses simple linear models updated adaptively to project the complex process dynamics and capture changes. The hierarchical approach uses the AGMPC controller to optimize PPB growth as the core of the system. The developed supervisory layer adjusts set-points for the core controller based on two operational scenarios: maximizing PPB concentration for quality, or increasing yield for quantity through effluent recycling. Lastly, due to competing PPB and non-PPB bacteria during start-up phase, an override strategy for this transition is investigated through simulation studies. The Purple Bacteria Model (PBM) simulates this process, and simulation results demonstrate the control system's effectiveness and robustness.

This paper focuses on the development of linear Switched Box–Jenkins (SBJ) models for approximating complex dynamical models of biological wastewater treatment processes. We discuss the adaptation of these processes to the SBJ framework, showing the model's generality and flexibility as a class of switched systems that can offer accurate predictions for complex and nonlinear dynamics. This approach of modeling enables real-time data reconciliation from experiments and allows the design of model-based control strategies. Through the extension of the Outer Bounding Ellipsoids (OBEs) algorithm, the paper introduces an online two-stage parameter identification algorithm that effectively handles bounded disturbances for SBJ models. Using the OBE method relaxes the stochastic assumptions on disturbances, which may not be satisfied in practice, particularly for biological and environmental fluctuations. The proposed decomposed OBE algorithm separately identifies the switching patterns and parameters of linear submodels, conducting parameter identification in two distinct phases for input/output and disturbance/output submodels. The efficacy of this approach is shown via simulation results validated against both ADM1 and PBM models, demonstrating the proposed algorithm's capability to accurately predict outputs from different biological wastewater treatment models.

This paper investigates the robustness of one innovative model-based method for leak detection, namely the Dual Model. We evaluate the algorithm’s performance under various leakage scenarios in the L-Town network, despite uncertainties and model mismatches in (i) base demand, (ii) pipe roughness, (iii) the number of sensors, and (iv) network topology. Our investigation results indicate that the Dual Model is highly sensitive to discrepancies in the first three parameters. However, the impact can be mitigated through sensor-specific calibration, such as adjusting sensor elevations. Moreover, the Dual Model has demonstrated robustness to minor topology mismatches, like those introduced by closed valves.

Correction to: Nature Energyhttps://doi.org/10.1038/s41560-022-01152-0, published online 24 October 2022 In the version of the article initially published, Gebrekidan Gebresilassie Eshetu’s name appeared incorrectly as Eshetu Gebrekidan Gebresilassie and has now been corrected in the HTML and PDF versions of the article.

Monitoring safe water access in developing countries relies primarily on household health survey and census data. These surveys are often incomplete: they tend to focus on the primary water source only, are spatially coarse, and usually happen every 5-10 years, during which significant changes can happen in urbanisation and infrastructure provision, especially in sub Saharan Africa. In this work, we present a data-driven approach that utilises and compliments survey based data of water access, to provide context-specific and disaggregated monitoring. The level of access to improved water and sanitation has been shown to vary with geographical inequalities related to the availability of water resources and terrain, population density and socio-economic determinants such as income and education. We use such data and successfully predict the level of water access in areas for which data is lacking, providing spatially explicit and community level monitoring possibilities for mapping geographical inequalities in access. This is showcased by applying three machine learning models that use such geographical data to predict the number of presences of water access points of eight different access types across Uganda, with a 1km by 1km grid resolution. Two Multi-Layer-Perceptron (MLP) models and a Maximum Entropy (MaxEnt) model are developed and compared, where the former are shown to consistently outperform the latter. The best performing Neural Network model achieved a True Positive Rate of 0.89 and a False Positive Rate of 0.24, compared to 0.85 and 0.46 respectively for the MaxEnt model. The models improve on previous work on water point modeling through the use of neural networks, in addition to introducing the True Positive - and False Positive Rate as better evaluation metrics to also assess the MaxEnt model. We also present a scaling method to move from predicting only the relative probability of water point presences, to predicting the absolute number of presences. To challenge both the model results and the more standard health surveys, a new household level survey is carried out in Bushenyi, a mid-sized town in the South-West of Uganda, asking specifically about the multitude of water sources. On average Bushenyi households reported to use 1.9 water sources. The survey further showed that the actual presence of a source, does not always imply that it is used. Therefore it is no option to rely solely on models for water access monitoring. For this, household surveys remain necessary but should be extended with questions on the multiple sources that are used by households.

Decarbonisation of the built environment is needed to abate the use of fossil fuels and greenhouse gas emissions. In the city of Amsterdam, multiple bottom-up initiatives have been initiated to reach these goals. In this paper, we explore how energy justice is reshaped by these initiatives on an urban scale. This is done by a case study on a platform that aims to connect, support and inform community energy initiatives. Based on ethnographic fieldwork performed between 2019 and 2022 on the heat transition in Amsterdam, we describe how relations between governmental bodies, businesses and urban residents are contested through this platform. Additionally, we describe how the platform shapes the access of citizens to decision-making spaces, financial tools and information to foster new forms of local autonomy, physical heating infrastructures and decision-making procedures. By analysing the motivations and activities for increasing users’ influence and ownership of resources with the notion of ‘commoning practices’, we show how activities of the platform do not only shape physical heating infrastructures, but also the decision-making processes for achieving low-carbon and renewable heating systems in Amsterdam. We, therefore, propose that the notion of ‘commoning practices’ can be used in future research to contribute to a dynamic understanding of how energy justice concerns are expressed and shaped in practice.

Purple phototrophic bacteria (PPB) show an underexplored potential for resource recovery from wastewater. Raceway reactors offer a more affordable full-scale solution on wastewater and enable useful additional aerobic processes. Current mathematical models of PPB systems provide useful mechanistic insights, but do not represent the full metabolic versatility of PPB and thus require further advancement to simulate the process for technology development and control. In this study, a new modelling approach for PPB that integrates the photoheterotrophic, and both anaerobic and aerobic chemoheterotrophic metabolic pathways through an empirical parallel metabolic growth constant was proposed. It aimed the modelling of microbial selection dynamics in competition with aerobic and anaerobic microbial community under different operational scenarios. A sensitivity analysis was carried out to identify the most influential parameters within the model and calibrate them based on experimental data. Process perturbation scenarios were simulated, which showed a good performance of the model.

The Netherlands is a low-lying country situated in the Rhine-Meuse delta. A significant portion of the Netherlands is located below sea level, making the proper management of local and national waterways essential. Polders are used to manage groundwater levels, drain excess rainwater, and store water during times of drought. These polders often have pumping stations that pump water into drainage canals, like the Noordzeekanaal-Amsterdam-Rijnkanaal (NZK-ARK), which receives water from the Rhine river and four local water authorities and connects to the North Sea at IJmuiden through a pumping station and a series of undershot gates.

The operators of the NZK-ARK utilize Model Predictive Control (MPC) to schedule the discharge of water through the gates and pumps. The combination of the pump and gate discharge allows the NZK-ARK to discharge excess water to the North Sea when the sea water level is both higher and lower than the water level in the canal. However, traditional MPC can lead to suboptimal schedules when uncertainty is introduced, resulting from, for example, incoming discharge, fluctuating electricity prices, and the availability of renewable energy. Stochastic MPC allows for the consideration of uncertainty in decision-making, optimizing control actions based on a range of potential scenarios. In the future, the objectives for the control system of the gates and pumps may become more complex and may need to take into account factors like renewable energy availability and electricity prices. Ensuring the effective and efficient management of water in the Netherlands is critical, and the use of polders for water storage and control of groundwater tables, and techniques like MPC and stochastic MPC play important roles in achieving this goal.

In this study, we present a framework that combines probabilistic forecasting, scenario generation and reduction, and stochastic MPC to minimize energy costs associated with pumping at the NZK-ARK. This framework is based on probabilistic forecasts of electricity prices and incoming discharge and is specifically designed for use at the NZK-ARK. By considering the uncertainty present in electricity prices and incoming discharge, our framework allows for the optimization of control actions through the use of stochastic MPC. The ultimate goal of this approach is to reduce energy costs at the NZK-ARK by effectively managing the discharge of water through the pumps and gates while complying with local constraints.

The design of strategic investments in water, energy and food (WEF) infrastructures is challenging because the size, location, technology mix and pace of development is made uncertain by multiple factors. For example, the return on investment, which comes long after building a hydropower dam, is made uncertain by local, regional and global climate and socio-economic factors. This is exacerbated by the challenges associated with the impacts of climate change, especially in sub-Saharan Africa (SSA) where it is difficult to model these impacts, hence leading to high levels of uncertainty in future scenarios (2050 and beyond).

Long-term investment planning and system operations for energy, depend on and compete with other sectors for, the availability of water (for hydropower and cooling thermal plants) and land resources (e.g. for biofuel production and arability). The efficient exploitation of land, energy and water resources and their synergised use for economic development therefore require an multidimensional integrated optimisation approach co-created with stakeholders in dialogue. This starts with planning followed by prioritised investments based on local, national and regional needs in the energy, agricultural and water sectors. This is mostly lacking in SSA at the moment. We gathered a selected group of experts in Accra, Ghana in November 2022 with a broad mix of experiences and expertise in the energy, water and agricultural sectors, who shared deeper insights and values of the need for integrated WEF planning to begin tackling challenges and opportunities identified in the Volta Basin in West Africa (starting with Ghana) and the Tana basin in Kenya. The main challenge identified was the disjointed planning of WEF infrastructures due to different financing mechanisms and siloed sectoral thinking; and participants raised emerging opportunities for planning infrastructure through transnational and regional cooperation as well as the need to build on existing and new initiatives devoid of entrenched political goals.

In this contribution, we will present some of the main findings from the meeting in Accra and share knowledge on how transparent WEF modelling can be contextualised for local operational relevance, and through co-creation, how interactive engagement tools can be used for planning, policy- and decision-making.

Dynamical systems and processes that either exhibit non-smooth behaviours (e.g. through logic control or natural phenomena) or work in different modes of operation are usually represented using hybrid systems models, i.e. mathematical models that combine continuous dynamics with discrete-event dynamics. Identification of a hybrid system includes finding switching patterns and identification of model parameters to obtain a data-driven model. This survey paper provides a systematic review of models (how to parameterize the system) and methods (how to identify unknown parameters) proposed for hybrid system identification with an exposition of recent advances and developments, and further research directions.

In this manuscript, we test the operational performance decrease of a probabilistic framework for Demand Response (DR). We use Day Ahead Market (DAM) price scenarios generated by a Combined Quantile Regression Deep Neural Network (CQR-DNN) and a Non-parametric Bayesian Network (NPBN) to maximise profit of a Battery Energy Storage System (BESS) participating on the DAM for energy arbitrage. We apply the generated forecast time series to a stochastic Model Predictive Control (MPC), and compare the performance using a point and perfect forecast. For the probabilistic forecasts, we test two control strategies; 1) minimising the Conditional Value at Risk (CVaR) for making costs, and 2) minimising the expected value of the cost. We apply the MPC in a closed-loop simulation setting and perform a sensitivity analysis of the profit by changing the ratio between battery capacity and the max power, the cluster reduction method, and the number of scenarios used by the MPC. We show that the proposed framework works, but the approach does not increase profit compared to a deterministic point forecast. This can possibly be explained by the deterministic forecast capturing the shape of the price curve with less noise than a probabilistic forecast without enough scenarios. We show that the value of a good forecast becomes smaller as the charging time of the battery becomes larger, due to the battery being unable to exploit small price differences optimally.