Toward improving water-energy-food nexus through dynamic energy management of solar powered automated irrigation system.

This paper focuses on developing a water and energy-saving reliable irrigation system using state-of-the-art computing, communication, and optimal energy management framework. The framework integrates real-time soil moisture and weather forecasting information to decide the time of irrigation and quantity of water required for potato crops, which is made available to the users across a region through the cloud-based irrigation decision support system. This is accomplished through various modules such as data acquisition, soil moisture forecasting, smart irrigation scheduling, and energy management scheme. The main emphasizes is on the electrical segment which demonstrates an energy management scheme for PV-battery based grid-connected system to operate the irrigation system valves and water pump. The proposed scheme is verified through simulation and dSpace-based real-time experiment studies. Overall, the proposed energy management system demonstrates an improvement in the optimal onsite solar power generation and storage capacity to power the solar pump which save the electrical energy as well as the water in order to establish an improved solar-irrigation system. Finally, the proposed system achieved water and energy savings of around 9.24 % for potato crop with full irrigation enhancing the Water-Energy-Food Nexus at field scale.

Integrating micro-algae into wastewater treatment: A review.

Improving the ecological status of water sources is a growing focus for many developed and developing nations, in particular with reducing nitrogen and phosphorus in wastewater effluent. In recent years, mixotrophic micro-algae have received increased interest in implementing them as part of wastewater treatment. This is based on their ability to utilise organic and inorganic carbon, as well as inorganic nitrogen (N) and phosphorous (P) in wastewater for their growth, with the desired results of a reduction in the concentration of these substances in the water. The aim of this review is to provide a critical account of micro-algae as an important step in wastewater treatment for enhancing the reduction of N, P and the chemical oxygen demand (COD) in wastewater, whilst utilising a fraction of the energy demand of conventional biological treatment systems. Here, we begin with an overview of the various steps in the treatment process, followed by a review of the cellular and metabolic mechanisms that micro-algae use to reduce N, P and COD of wastewater with identification of when the process may potentially be most effective. We also describe the various abiotic and biotic factors influencing micro-algae wastewater treatment, together with a review of bioreactor configuration and design. Furthermore, a detailed overview is provided of the current state-of-the-art in the use of micro-algae in wastewater treatment.

Hedging as an adaptive measure for climate change induced water shortage at the Pong reservoir in the Indus Basin Beas River, India.

This study investigated the adaptive capacity of static and dynamic hedging operating policies to shore up the performance, i.e. reliability and vulnerability, in irrigation water supply of Pong reservoir in India, during climate change. The policies were developed using genetic algorithm optimisation and used to force reservoir simulations for different climate change perturbed inflow series, whence derive the performance. For static hedging, the hedging fraction remains constant throughout the year while for dynamic hedging, this fraction varies monthly or seasonally. Results showed that static hedging was effective at tempering the systems vulnerability from its high of ≥60% to lower than 25%, while maintaining an acceptable volume-based reliability. Further simulations with dynamic hedging provided only modest improvements in these two indices. The significance of this study is its demonstration of the effectiveness of hedging as a climate change adaptation measure by limiting water shortage impacts. It also demonstrates that simple static hedging can match more complex dynamic hedging policies.

Anaerobic digestion process modeling using Kohonen self-organising maps.

Anaerobic digestion is a versatile method for wastewater treatment as it not only reduces the waste but also leads to production of renewable energy. Modeling of the anaerobic process requires knowledge of biological and physico-chemical conditions, bacterial growth kinetics, substrate utilization, and product synthesis. However, the complexity of the process calls for highly sophisticated models requiring very high level of expertise and knowledge in the subject. This paper presents an approach for modeling of anaerobic digestion process through which the correlation between various process parameters can be studied, knowledge can be extracted, and system behaviour can be predicted. The datasets have been generated using a synthetic Matlab-Simulink-Excel model and process modelling is done using Kohonen Self organizing maps (KSOM). The resulting KSOM provided a visual interpretation of the inter-relationships between parameters (OLR, Sac, pH, Shco3, Q, Sglu_in, Qgas_out, Sglu_out, and Sch4_gas_out) which would help semi-skilled operators for operation and control of such plants. The model accurately predicts the variations in methane and total gas output with respect to changes in input parameters as the correlation is more than 90% for most of the parameters. This methodology offers a platform for scientists and researchers in comprehending the system behaviour under various operating conditions, even with missing data.

Untangling the water-food-energy-environment nexus for global change adaptation in a complex Himalayan water resource system.

Holistic water management approaches are essential under future climate and socio-economic changes, especially while trying to achieve inter-disciplinary societal goals such as the Sustainable Development Goals (SDGs) of clean water, hunger eradication, clean energy and life on land. Assessing water resources within a water-food-energy-environment nexus approach enables the relationships between water-related sectors to be untangled while incorporating impacts of societal changes. We use a systems modelling approach to explore global change impacts on the nexus in the mid-21st century in a complex western Himalayan water resource system in India, considering a range of climate change and alternative socio-economic development scenarios. Results show that future socio-economic changes will have a much stronger impact on the nexus compared to climate change. Hydropower generation and environmental protection represent the major opportunities and limitations for adaptation in the studied system and should, thereby, be the focus for actions and systemic transformations in pursue of the SDGs. The emergence of scenario-specific synergies and trade-offs between nexus component indicators demonstrates the benefits that water resource systems models can make to designing better responses to the complex nexus challenges associated with future global change.

Applying Kohonen self-organizing map as a software sensor to predict biochemical oxygen demand.

The 5 days at 20 degrees C biochemical oxygen demand (BOD5) is an important parameter for monitoring organic pollution in water and assessing the biotreatability of wastewater. Moreover, BOD5 is used for wastewater treatment plant discharge consents and other water pollution control purposes. However, the traditional bioassay method for estimating the BOD5 involves the incubation of sample water for 5 days. It follows that BOD5 is not available for real-time decisionmaking and process control purposes. On the other hand, previous efforts to solve this problem by developing more rapid biosensors had limited success. This paper reports on the development of Kohonen self-organizing map (KSOM)-based software sensors for the rapid prediction of BOD5. The findings indicate that the KSOM-based BOD5 estimates were in good agreement with those measured using the conventional bioassay method. This offers significant potential for more timely intervention and cost savings during problem diagnosis in water and wastewater treatment processes.