Dr Michael Short

The decarbonisation of residential heating systems has become increasingly important to meet the global goals of minimising carbon emissions and combating climate change. However, with rising energy costs, this can be a significant challenge for low-income households. This study presents a novel optimisation framework to aid the decarbonisation of residential heating in the United Kingdom by combining technology-related decision-support with policy decisions. The framework can recommend the optimal retrofit of low-carbon heating technologies and fabric improvement measures such as insulation upgrades for improving energy efficiency. Concurrently, the optimal financial contributions towards investment costs from grants supporting low-income households and social housing is determined. It also includes piecewise linearisations to capture the detailed operation of air source heat pumps, which are set to replace natural gas-based heating systems, and assesses the eligibility of each dwelling for grant funding. A large case study consisting of social housing stock in Woking, UK, has been used to test the framework. Three scenarios are used to assess the efficacy of existing technology and policy combinations to meet local emissions reduction targets, which are benchmarked against emissions from existing gas-based heating systems and insulation measures. Results highlight the limitations of existing UK grants, as these can only achieve an emissions reduction of 33.5% without incurring significant additional investment costs to the local council. The lack of support towards installing hot water tanks, which are required for the operation of heat pumps, is another major limitation in existing grants. A proposed scenario, which introduces a fictional grant with unlimited funding, sheds light on the much larger grant contributions expected to achieve an emissions reduction of 66.8%, which surpasses local targets. These results also suggest the need for operational support to cope with much higher energy bills, especially for low-income and/or fuel-poor households, due to the electrification of heating systems. Overall, the framework is a useful tool for local councils, policy makers, and other stakeholders to make informed decisions on the affordable decarbonisation of residential heating systems.

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.

The delay in action to mitigate climate change has compounded its impacts and there is now a greater dependence on carbon dioxide removal (CDR) to achieve net-zero carbon targets by 2050. This work develops a software framework for optimal decarbonised energy planning to determine the optimum deployment of renewable energy sources, negative emission technologies (NETs), and CO2 capture and storage (CCS), which has been underexplored in previous work. The novel mathematical optimisation-based tools in this work provide rigorous guarantees on the qualities of the solution, subject to planning constraints i.e., budget etc. The application of the software framework is demonstrated with a case study containing seven power plants. In this multiperiod work, CCS deployment is favoured for coal-based power plants due to its highest carbon intensity, while energy-producing NETs (EP-NETs) is deployed for all periods.

The criticality of climate change is such that any further delay in mitigation action would result in irreversible damage. This work develops a novel software framework for optimal decarbonisation in energy planning to determine the optimum deployment of renewable energy sources, alternative low carbon fuels, negative emission technologies (NETs) and CO2 capture and storage (CCS). The mathematical programming-based tools in this work provide rigorous optimal solutions, subject to budget, demand, and emission constraints. The application of the software framework is demonstrated with a Malaysian energy decarbonisation case study. The results indicate a heavy reliance on NETs, alongside reductions in coal and natural gas use to achieve CO2 neutrality by 2050.

Many autonomous microgrids have extensive penetration of distributed generation (DG). Optimal power flow (OPF) is necessary for the optimal dispatch of networked microgrids (NMGs). Existing convex relaxation methods for three-phase OPF are limited to radial networks. In light of this, we develop a semidefinite programming (SDP) convex relaxation model which can cope with meshed networks and also includes a model of three-phase DG and under-load voltage regulators with different connection types. The SDP model solves the OPF problem of multi-phase meshed network effectively, with satisfactory accuracy, as validated by real 6-bus, 9-bus, and 30-bus NMGs, and the IEEE 123-bus test cases. In the SDP model, the convex symmetric component of the three-phase DG model is demonstrated to be more accurate than a three-phase DG modelled as three single-phase DG units in three-phase unbalanced OPF. The proposed method also has higher accuracy than the existing convex relaxation methods. The resultant optimal control variables obtained from the convex relaxation optimization can be used for both final optimal dispatch strategy and initial value of the non-convex OPF to obtain the globally optimal solution efficiently.

To the editor – The Glasgow Agreement statement on the " phase-down " of coal means that limiting warming to well below 2C will require more ambitious emissions cuts through other means. To achieve mid-century carbon neutrality, aggressive policies are needed to reduce point-source emissions primarily through measures such as increased use of renewables. Since many developing nations will be unable to make this transition by 2050, negative emissions technologies (NETs) will also have to be scaled up rapidly to offset residual emissions from fossil fuels through carbon dioxide removal (CDR) 1,2. Many start-ups have followed the trend of the emerging "drawdown economy" to commercialize NETs through the sale of credits from CDR, but there remains a gap in providing top-down decision support for their strategic deployment considering local and regional constraints. The ramp-up of CDR to the gigaton scale has serious implications for energy systems due to the electricity requirement of many NET alternatives 3. Timing is also critical; recent modelling results focusing on bioenergy with carbon capture and storage and direct air capture show that early deployment can reduce cumulative costs of achieving net-zero emissions in the European Union 4. Exploring diverse NETs portfolios is critical due to their potential to avoid risks in large-scale deployment 5. For example, vast tracts of monoculture plantations for bioenergy with carbon capture and storage (BECCS) can threaten biodiversity 6. No single option can sustainably meet the climate targets, so multiple NETs at moderate scales are needed 7. However, most research papers still focus on individual NETs, with few taking a system-level outlook. The global and regional potential capacities of NETs have been evaluated individually rather than as part of a carbon management portfolio 8,9. Integrated assessment modelling (IAM) studies have focused mainly on BECCS and afforestation and reforestation (AR) analyzed separately 10. Only one IAM study to date has attempted the optimization of a NETs portfolio consisting of BECCS, direct air carbon capture and storage (DACCS), enhanced weathering, and AR 11. The results of the study show that a full portfolio supports the attainment of net-zero faster and with a better balance of regional CDR contributions. Instead of assuming a predefined set of NETs for decarbonization, portfolio optimization approaches will be essential for enabling rapid scale-up to meet net-zero targets (Fig. 1). New models are urgently required to determine the optimal mix of NETs to maximize CDR within cumulative cost, natural resource, and sustainability limits while accounting for the trade of carbon credits. Process integration techniques originally developed to improve the energy and resource efficiency of industrial plants can be adapted for this purpose 12. In the previous decades, various mathematical models and algorithms from process integration have been extended to industrial decarbonization through the optimum allocation of renewable energy resources and optimal planning of CO2 capture and storage 13. Other variants have also been developed to optimize research and development portfolios of low-carbon technologies 14. Such techniques have only been recently developed for the optimal deployment of NETs considering impacts on background energy systems 15 as well as cost and environmental footprints 16. These early works demonstrate the promising potential of this class of models, but much more research remains to be done so they can be used to accelerate the scale-up of NETs in the coming decades.

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.

Biomass resources have the potential to become a viable renewable technology and play a key role within the future renewable energy paradigm. Since CO2 generated in bio-energy production is equal to the CO2 absorbed during the growth of the biomass, this renewable energy is a net zero emissions resource. Biomass gasification is a versatile method for transforming waste into energy in which biomass material is thermochemically converted within a reactor. Gasification's superior flexibility, including both in terms of biomass type and heat generation or energy production alternatives, is what stimulates biomass gasification scientific and industrial potential. Downdraft gasifiers seem to be well for small-scale generation of heat along with energy, whereas fluidised bed and entrained flow gasifiers currently attain significant economies of scale for fuel production. The operation of gasifiers is influenced by several factors, including operational parameters, feedstock types, and reactor design. Modelling is a valuable tool for building a unit based on the results of model prediction with different operational parameters and feedstock in such scenarios. Once verified, a suitable model may be used to assess the sensitivity of a gasifier's performance to changes in various operational and design factors. Effective models may help designers to theorise and predict the impacts of a variety of characteristics without the need for further empirical observations, which can help in the design and implementation of this technology. This work provides an overview of gasification technologies and a succinct guidance to the modelling decisions and modelling strategies for biomass gasification to enable a successful biomass to fuel conversion. A technical description and critical analysis of thermodynamic, stoichiometric, computational fluid dynamic and data-driven approaches is provided, including crucial modelling considerations that have not been explored in earlier studies. The review aims to aid researchers in the field to select the appropriate approach and guide future work.

This paper presents a systematic synthesis method that considers multiple shells and logarithmic mean temperature difference (LMTD) FT correction factor for heat exchanger networks (HENs) involving multiple periods of operation. The approach adopted entails firstly generating a reduced multiperiod HEN superstructure using network solutions obtained when the STEP (Stream Temperature Versus Enthalpy Plot) and HEAT (Heat Allocation and Targeting) synthesis methods are applied to each subperiod. The second stage entails generating an initial multiperiod HEN solution from the reduced superstructure synthesis approach. The number of shells, as well as the FT correction factor, required by each exchanger in each period of the initial multiperiod HEN are then manually calculated and used to initialise the multiperiod HEN to obtain updated representative heat exchanger areas for all stream pairs in all periods of operations. The solution obtained, when the method of this paper is applied to a literature example, shows that the assumption of 1 – 1 (1 shell pass – 1 tube pass) design configuration for multiperiod HEN problems, underestimates the representative heat exchanger areas by 12.3%.

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.

We propose a new strategy to synthesize heat exchanger networks with detailed designs of individual heat exchangers. The proposed strategy uses a multistep approach by first obtaining a heat exchanger network topology through solving a modified version of the mixed integer nonlinear programming (MINLP) stage‐wise superstructure of Yee and Grossmann, which includes a smoothed LMTD approximation and pressure drops. In a second nonlinear programming (NLP) suboptimization step, we allow for nonisothermal mixing to solve problems with or without exchanger bypasses. The selected heat exchangers along with the mass and energy balances obtained are then used to design the network with detailed exchanger designs through solving a sequence of NLPs for individual heat exchanger designs. The NLPs are based on the detailed discretized optimization models of Kazi et al., which solve quickly and reliably to obtain heat exchangers based on rigorous, first‐principles derived coupled differential equations. These models solve a differential algebraic equation system and do not rely on usual assumptions associated with other heuristic‐based exchanger design methods, such as log mean temperature difference and FT correction factors. These detailed exchanger designs are then used to update the network optimization model through sets of correction factors on heat exchanger area, number of shells, heat transfer coefficients, and pressure drops of each exchanger design, in a method based on that of Short et al. The method solves reliably, guaranteeing feasible exchangers for every potential network generated by the shortcut models, through validation with rigorous heat exchanger models at every iteration. In addition, the method does not increase the nonlinearity of the MINLP model, nor does it require any manual intervention or initialization from the user. Three examples are solved and the results are compared to those obtained in the literature.

This chapter presents a synthesis method for multi‐period combined heat and mass exchange networks (CHAMENs). The mass exchange network involves a regeneration network (REN) superstructure with multiple recyclable mass‐separating agents (MSAs) and multiple regenerating streams. The CHAMENs superstructure with REN is integrated with utilities generated from both renewable and fossil‐based energy sources. Hence, the environmental impact of such networks is studied. Lastly, the proposed CHAMEN model is extended to handle multi‐objective optimization (MOO) of both environmental impact and economics, to identify an optimum network configuration.

Developing a flexible heat exchanger network, that will remain operable in the face of potential variations in stream parameters, or variables, around some nominal values, especially for large problems, are difficult to solve simultaneously. A hybrid synthesis approach that systematically combines sequential and mathematical programming techniques may be more suitable to adopt. The proposed method presented in this paper entails firstly generating two representative single period networks using the Pinch Technology Stream Temperature versus Enthalpy Plot (STEP) approach and the multi-period stage-wise superstructure model for heat exchanger network synthesis. Streams for the representative network are obtained using the largest stream heat demand as criteria. The second stage of the proposed method entails generating a reduced multi-period stage-wise superstructure, using a combination of the matches obtained in the single period representative networks of the first step as initialising matches. The solution of the reduced superstructure of the second step, which is solved as a Mixed Integer Non-Linear Programming (MINLP) model, is then selected as the best multi-period network. The newly developed method of this paper is tested using an example from the literature. One of the solutions obtained is only 0.9 % higher, in terms of total annual cost, than the best solution presented in the literature but has the benefit of a fewer number of units.

•Detailed heat exchanger models are embedded within a heat exchanger network synthesis optimization.•A trust region filter algorithm is used as a surrogate modelling strategy for HENS for the first time to incorporate pressure drops, numbers of shells, etc.•An integer-cut strategy algorithm is introduced to explore different topologies.•Results are compared to existing methods for solving HENS with detailed models, demonstrating excellent performance. We develop a trust region filter strategy for simultaneous optimal design of heat exchanger networks that includes detailed design of shell-and-tube heat exchangers. The strategy first solves a mixed-integer nonlinear programming (MINLP) formulation with shortcut models to generate candidate network topologies, which are then used in a non-isothermal mixing nonlinear programming (NLP) suboptimization with detailed optimal exchanger design models embedded using a modified trust region filter (TRF) algorithm. An integer cut based strategy is used to bound the solutions from MINLP and the NLP which aids in convergence to the solution of the overall simultaneous design problem. Under assumptions, the TRF based strategy can guarantee convergence to near optimal solutions of the overall design problem. The presented solution strategy is thus able to find optimal heat exchanger network designs based on the simultaneous optimization of the network topology and mass and energy balances, together with detailed shell-and-tube heat exchanger optimization, including the number of shell and tube passes, pressure drops, and tubes, tube lengths, etc. The proposed strategy is tested on three literature based case studies and their results are compared with previous studies to showcase its performance.

Multivariate spectroscopic data is increasingly abundant in the chemical and pharmaceutical industries. However, it is often challenging to estimate reaction kinetics directly from it. Recent advances in obtaining kinetic parameter estimates from spectroscopic data based on large-scale nonlinear programming (NLP), maximum likelihood principles, and discretization on finite elements lead to increased speed and efficiency (Chen et al., 2016). These new techniques have great potential for widespread use in parameter estimation. However they are currently limited due to their applicability to relatively small problem sizes. In this work, we extend the open-source package for estimating reaction kinetics directly from spectra or concentration data, KIPET, for use with multiple experimental datasets, or multisets (Schenk et al., 2020). Through a detailed initialization scheme and by taking advantage of large-scale nonlinear programming techniques and problem structure, we are able to solve large problems obtained from multiple experiments, simultaneously. The enhanced KIPET package can solve problems wherein multiple experiments contain different reactants and kinetic models, different dataset sizes with shared or unshared individual species’ spectra, and can obtain confidence intervals quickly based on the NLP sensitivities. In addition, we propose a new variance estimation technique based on maximum likelihood derivations for unknown covariances from two sample populations. This new variance estimation technique is compared to the previously proposed iterative-heuristics-based algorithm of Chen et al. (2016) for distinguishing between variances of the noise in model variables and in the spectral measurements. We demonstrate the new techniques on a variety of example problems, with sample code, to show the utility of the approach and its ease of use. We also include the curve-fitting problem to cases where we have concentration data given directly, and are required to estimate kinetic parameters across multiple experimental datasets.

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.

Mass and heat integration are important to achieving economically and environmentally sustainable processes through increased efficiency. Typically, heat and mass exchange networks are solved separately using process integration techniques such as pinch technology or formulating nonconvex mixed-integer nonlinear programming (MINLP) problems, which are challenging to solve. To simplify the MINLP, shortcut models are employed, which can result in under/overestimation of the real network, leading to suboptimal or infeasible designs. We introduce a new optimisation algorithm for combined heat and mass exchanger network synthesis (CHAMENS), including detailed design models. The method uses shortcut models in an MINLP to find network topology, followed by a nonlinear programming (NLP) suboptimisation. The NLP allows non-isothermal and non-isocompositional mixing, uses detailed unit models of packed columns based on orthogonal collocation on finite elements (OCFE), and detailed shell and tube heat exchanger designs. We incorporate a differential-algebraic equation (DAE) based shell and tube heat exchanger design model via surrogates in a trust region filter (TRF) framework, guaranteeing optimal solutions for the detailed exchanger models are found by the surrogate models. We demonstrate the proposed approach on a case study, showcasing its performance and the need to incorporate detailed unit models in topology optimisation to find practical optimal designs.

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.

This work provides the first systematic critical review of mass exchanger network synthesis literature. Mass exchanger networks play a central role in many pollution reduction and resource utilisation processes and contain many complex decisions to be made including exchanger types, sizing, and mass separating agent selection. We present a comprehensive review of the key milestones in the development of methods for mass exchanger network synthesis and focus on the key challenges that have hindered research in this area from flourishing in the manner of the conceptually similar heat exchanger network synthesis problem. We find that several important research questions remain for the methods to find wider use in industry. More efficient techniques for solving nonconvex mixed-integer nonlinear programs and better methods of including more accurate, higher-order unit models for industrial problems within network optimisation problems are particularly important, as current methods provide highly simplified unit representations that do not take into account many important practical design considerations that have significant cost implications. Furthermore, we identify significant potential for further research into increasing the scope of the problem to include issues such as flexibility and controllability, inter-plant mass exchanger networks, batch processes, retrofit and further integration of heat and mass exchanger networks, with research into these domains limited. Through further research of these under-developed applications of mass exchanger network synthesis, we envision that techniques for mass integration can become a powerful tool to enhance mass integration techniques for sustainable cleaner production technology. •Existing synthesis methods have been mainly graphical and deterministic.•More robust methods should be developed for multicomponent systems.•Most methods have included simplified unit designs rather than detailed ones.•Controllability and flexibility despite being crucial have received less attention.•Opportunities for interplant mass integration should be explored.

A trust region framework is presented to synthesize heat exchanger network with detailed exchanger designs. The heat exchanger network (HEN) is first synthesized using the stage wise superstructure (SWS) formulation of Yee and Grossmann (1990). After a topology is found in this step the heat exchangers and the connections, flows and intermediate temperatures are designed, using the first principles based differential algebraic (DAE) model presented in our previous work. These detailed DAEs for heat exchanger design are incorporated within a nonlinear programming (NLP) model using reduced order models and solved using NLP solver IPOPT with a trust region method. The results show that the new method is faster than the previous approaches while providing comparable results.

A new method for the detailed design of shell and tube heat exchangers is presented through the formulation of coupled differential heat equations, along with algebraic equations for design variables. Heat exchanger design components (tube passes, baffles, and shells) are used to discretize the differential equations and are solved simultaneously with the algebraic design equations. The coupled differential algebraic equation (DAE) system is suitable for numerical optimization as it replaces the nonsmooth log mean temperature difference (LMTD) term. Discrete decisions regarding the number of shells, fluid allocation, tube sizes, and number of baffles are determined by solving an LMTD‐based method iteratively. The resulting heat exchanger topology is then used to discretize the detailed DAE model, which is solved as a nonlinear programming model to obtain the detailed exchanger design by minimizing an economic objective function through varying the tube length. The DAE model also provides the stream temperature profiles inside the exchanger simultaneously with the detailed design. It is observed that the DAE model results are almost equal to the LMTD‐based design model for one‐shell heat exchangers with constant stream properties but shows significant differences when streams properties are allowed to vary with temperature or the number of shells are increased. The accuracy of the solutions and the required computational costs show that the model is well suited for solving heat exchanger network synthesis problems combined with detailed exchanger designs, which is demonstrated in Part 2 of the paper.

Mass exchanger networks (MENs) are used to remove/recover contaminants from polluted streams through absorption with available process streams or external mass separating agents. Process Integration techniques such as Pinch Technology (PT) or mathematical optimisation can be used to synthesise optimal networks, however a lack of accessible software and difficulties in formulating the non-convex problem has stunted research. This article presents an open-source Python package for the synthesis of optimal MENs. The package uses the algebraic modelling language, Pyomo, and takes advantage of Python’s object-oriented nature to solve a series of optimisation problems, improving on the performance of previous approaches to the problem of incorporating detailed unit designs into MEN synthesis. The package uses automated initialisation strategies to first solve a superstructure-based mixed-integer nonlinear program (MINLP). Thereafter, a detailed optimisation model, formulating the packed column as a system of differential-algebraic equations, is used to design the columns. This detailed packed column design is used to update the MINLP through correction factors, driving the network solution towards the detailed unit optimisation solutions. The new software, called MExNetS, implements this strategy in a user-friendly package that is easily modified and well-documented. In addition to the new software implementation, novel strategies are employed to ensure feasibility at each iteration, which is a challenge in these non-convex optimisation formulations, and new binary cuts are generated and applied to the MINLP that can significantly speed up convergence compared to the previous study. The package also contains automatic superstructure generation based on user-inputted data, with the hope that this software can inspire further research in this area and be accessible to practitioners.

This paper presents KIPET (Kinetic Parameter Estimation Toolkit) an open-source toolbox for the determination of kinetic parameters from a variety of experimental datasets including spectra and concentrations. KIPET seeks to overcome limitations of standard parameter estimation packages by applying a unified optimization framework based on maximum likelihood principles and large-scale nonlinear programming strategies for solving estimation problems that involve systems of nonlinear differential algebraic equations (DAEs). The software is based on recent advances proposed by Chen et al. (2016) and puts their original framework into an accessible framework for practitioners and academics. The software package includes tools for data preprocessing, estimability analysis, and determination of parameter confidence levels for a variety of problem types. In addition KIPET introduces informative wavelength selection to improve the lack of fit. All these features have been implemented in Python with the algebraic modeling package Pyomo. KIPET exploits the flexibility of Pyomo to formulate and discretize the dynamic optimization problems that arise in the parameter estimation algorithms. The solution of the optimization problems is obtained with the nonlinear solver IPOPT and confidence intervals are obtained through the use of either sIPOPT or a newly developed tool, k_aug. The capabilities as well as ease of use of KIPET are demonstrated with a number of examples.