Ishanki Anjana De Mel

Multi-period Heat Exchanger Networks (HENs) are designed as heat recovery energy efficient systems over a set of operating conditions for process streams. The problem becomes more complex when detailed exchanger designs are accounted for in the network synthesis problem. Typically, in mixed-integer nonlinear programming (MINLP) multi-period HEN optimisation, the maximum area heat exchanger across all periods is considered. However, when considering detailed designs, often this exchanger is unsuitable for operation over all periods. In this study, a trust-region algorithm is proposed to incorporate detailed exchanger designs for multi-period operation. The exchanger design is modelled using surrogate models inside a network-level NLP model which is derived from the multi-period MINLP HENS model solution. The method is applied to a case study and the results show the effectiveness of the proposed algorithm.

The design and operation of distributed energy systems (DES) have often been modelled as linear optimisation problems. Although DES are increasingly connected to existing alternating current (AC) distribution networks, state-of-the-art DES modelling frameworks use oversimplified approximations which either exclude network constraints or overlook the inherent three-phase unbalance present in distribution networks. This can lead to poor designs which amplify network operational issues and result in greater costs to both the network and consumers/producers. This study presents a new modelling framework for DES design, which incorporates unbalanced optimal power flow within DES models for the first time. Furthermore, Robust Optimisation is included in this detailed modelling framework to ensure design feasibility under worst-case scenarios. Results show that previous frameworks tend to either overestimate or underestimate objectives when compared with the DES model combined with unbalanced power flow. Robust scenarios demonstrate that the new combined model is capable of closing the gap between objectives when compared with a linear DES-only model, albeit with different designs that do not violate grid constraints during baseline operation. These results suggest that this detailed framework can be utilised for DES design and network planning, as it produces more robust designs which can potentially help avert operational issues.

Gastric emptying (GE) is the process of food being processed by the stomach and delivered to the small intestine where nutrients such as lipids are absorbed into the blood circulation. The combination of an easy and inexpensive method to measure GE such as the CO2 breath test using the stable isotope [ 13 C]octanoic acid with semi-mechanistic modelling could foster a wider application in nutritional studies to further understand the metabolic response to food. Here, we discuss the use of the [ 13 C]octanoic acid breath test to label the solid phase of a meal, and the factors that influence GE to support mechanistic studies. Furthermore, we give an overview of existing mathematical models for the interpretation of the breath test data and how much nutritional studies could benefit from a physiological based pharmacokinetic model approach.

The year 2020 has seen the emergence of a global pandemic as a result of the disease COVID-19. This report reviews knowledge of the transmission of COVID-19 indoors, examines the evidence for mitigating measures, and considers the implications for wintertime with a focus on ventilation.

Distributed Energy Systems (DES) are set to play a vital role in achieving emission targets and meeting higher global energy demand by 2050. However, implementing these systems has been challenging, particularly due to uncertainties in local energy demand and renewable energy generation, which imply uncertain operational costs. In this work we are implementing a Mixed-Integer Linear Programming (MILP) model for the operation of a DES, and analysing impacts of uncertainties in electricity demand, heating demand and solar irradiance on the main model output, the total daily operational cost, using Global Sensitivity Analysis (GSA). Representative data from a case study involving nine residential areas at the University of Surrey are used to test the model for the winter season. Distribution models for uncertain variables, obtained through statistical analysis of raw data, are presented. Design results show reduced costs and emissions, whilst GSA results show that heating demand has the largest influence on the variance of total daily operational cost. Challenges and design limitations are also discussed. Overall, the methodology can be easily applied to improve DES design and operation.

Optimisation models for the design of distributed energy systems (DES) often exclude inherent nonlinearities and constraints associated with alternating current (AC) power flow and the underlying distribution network. This study aims to assess this gap by comparing the performance of linear and nonlinear formulations of DES design models, connected to and trading with an AC grid. The inclusion of the optimal power flow (OPF) constraints within the DES design framework is demonstrated in the methodology. A residential case study is used to test both models and compare the designs obtained from the two formulations. The results highlight that DES designs obtained are different when constraints related to the underlying distribution network are added, particularly when electricity storage is not considered. Overall, this study highlights the need for future modelling efforts to include OPF within DES optimisation frameworks to obtain practically feasible designs, rather than considering them as standalone problems.

Optimisation and simulation models for the design and operation of grid-connected distributed energy systems (DES) often exclude the inherent nonlinearities related to power flow and generation and storage units, to maintain an accuracy-complexity balance. Such models may provide sub-optimal or even infeasible designs and dispatch schedules. In DES, optimal power flow (OPF) is often misrepresented and treated as a standalone problem. OPF consists of highly nonlinear and nonconvex constraints related to the underlying alternating current (AC) distribution network. This aspect of the optimisation problem has often been overlooked by researchers in the process systems and optimisation area. In this review we address the disparity between OPF and DES models, highlighting the importance of including elements of OPF in DES design and operational models to ensure that the design and operation of microgrids meet the requirements of the underlying electrical grid. By analysing foundational models for both DES and OPF, we identify detailed technical power flow constraints that have been typically represented using oversimplified linear approximations in DES models. We also identify a subset of models, labelled DES-OPF, which include these detailed constraints and use innovative optimisation approaches to solve them. Results of these studies suggest that achieving feasible solutions with high-fidelity models is more important than achieving globally optimal solutions using less-detailed DES models. Recommendations for future work include the need for more comparisons between high-fidelity models and models with linear approximations, and the use of simulation tools to validate high-fidelity DES-OPF models. The review is aimed at a multidisciplinary audience of researchers and stakeholders who are interested in modelling DES to support the development of more robust and accurate optimisation models for the future.