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Abstract Post-combustion solvent-based carbon capture is a promising technology that potentially can offset the greenhouse gas emissions from fossil-driven power generation systems. The challenge is that CO2 absorption (similar to other CCS technologies) imposes energetic penalties, and constrains the operational flexibility. In this paper, we build upon our recent contributions in the field (Sharifzadeh et al., 2016; Sharifzadeh and Shah, 2016), and study the dynamic response of such process to the electricity load changes in the power plant. The key research question is to investigate if the steady-state integrated process design and control framework applied in the previous studies, can also ensure controllability under a wide range of disturbances. The present study considers the mutual interactions between the power plant and capture process. Other features of interest include the implications of key design and operational decisions such as reboiler temperature, solvent circulation flow rate, solvent concentration and the rate of power load change or CO2 setpoint tracking for flexible process operation. The results suggest that the capture process exhibits a high degree of flexibility and the integrated design and control framework could be the key enabler for the commercialization of post-combustion solvent-based carbon capture.

This study builds a holistic, transparent life cycle assessment model of a variety of aqueous mineral carbonation processes using a hybrid process model and economic input–output life cycle assessment approach (hybrid EIO-LCA). The model allows for the evaluation of the tradeoffs between different reaction enhancement processes while considering the larger lifecycle impacts on energy use and material consumption. A preliminary systematic investigation of the tradeoffs inherent in mineral carbonation processes is conducted to provide guidance for the optimization of the life-cycle energy efficiency of various proposed mineral carbonation processes. The life-cycle assessment of aqueous mineral carbonation suggests that a variety of alkalinity sources and process configurations are capable of net CO2 reductions. The total CO2 storage potential for the alkalinity sources considered in the U.S. ranges from 1.8% to 23.7% of U.S. CO2 emissions, depending on the assumed availability of natural alkalinity sources and efficiency of the mineral carbonation processes.

Whether the thermal sensitivity of an organism's traits follows the simple Boltzmann-Arrhenius model remains a contentious issue that centers around consideration of its operational temperature range and whether the sensitivity corresponds to one or a few underlying rate-limiting enzymes. Resolving this issue is crucial, because mechanistic models for temperature dependence of traits are required to predict the biological effects of climate change. Here, by combining theory with data on 1,085 thermal responses from a wide range of traits and organisms, we show that substantial variation in thermal sensitivity (activation energy) estimates can arise simply because of variation in the range of measured temperatures. Furthermore, when thermal responses deviate systematically from the Boltzmann-Arrhenius model, variation in measured temperature ranges across studies can bias estimated activation energy distributions toward higher mean, median, variance, and skewness. Remarkably, this bias alone can yield activation energies that encompass the range expected from biochemical reactions (from ~0.2 to 1.2 eV), making it difficult to establish whether a single activation energy appropriately captures thermal sensitivity. We provide guidelines and a simple equation for partially correcting for such artifacts. Our results have important implications for understanding the mechanistic basis of thermal responses of biological traits and for accurately modeling effects of variation in thermal sensitivity on responses of individuals, populations, and ecological communities to changing climatic temperatures.

Energy demand modelling has been widely applied in various contexts, including power plant generation, building energy simulation and demand-side management. However, it is still an ongoing research topic in terms of the choice of modelling method, feature engineering for data-driven methods, the application contexts and the type of data used. In the residential sector, survey-based and meter-based approaches are categorised according to the type of input data used, i.e. the activity records from the time use survey and energy consumption from meters respectively. These two paradigms are not necessarily easy to combine, which warrants the questions of when one may be preferred over the other and whether they need to be combined despite the significant data requirements. Other details also have a huge impact on the data structure and performance of the energy demand model, including the choice of influential factors, the historical time window of factors selected, the split between training and test data, and the choice of machine learning (ML) algorithm. There is a lack of comparative research to guide researchers and practitioners in developing energy demand modelling capability, specifically as it pertains to these issues. This study analyses three groups of test scenarios in a multi-household residential context based in the UK. Six ML algorithms (LightGBM, Random forest, ANN, SVM, KNN and LSTM), with eight sets of various influential features, at four different historical time window widths and two train-test splits were compared. An appropriate methodology was designed to capture the temporal impact of activities on energy demand and represent the overlap and interaction of activities. The results show that the combination of meter-based and survey-based energy demand models performs better in terms of modelling accuracy and robustness against sudden load variation. Particularly, integrating energy tariffs, household and individual attributes, appliance usage and general activity features can improve the energy demand model. Among the ML algorithms, LightGBM and ANN perform better than other algorithms while LSTM and SVM may not be suitable in this multi-household short monitoring context.

Abstract Cost-effectively decarbonising the power sector and household energy use using variable renewable energy will require that electricity consumption becomes much more flexible and responsive to constraints in supply and the distribution network. In recent years residential demand response (DR) has received increasing attention that has sought to answer, based on current evidence, questions about how much consumers will engage with DR. This paper critically reviews the evidence base for residential consumer engagement with DR and draws out several important limitations in it. We argue for a more action-oriented focus on developing practical strategies to enable and unlock greater loadshifting and consumer engagement with DR within a changing technology and regulatory context. A number of recommendations are put forward for accelerating UK consumer engagement with DR, presented under three broad strategies: (a) promote awareness of smart tariffs, smart meters and storage and automation behind-the-meter devices as mutually-supportive components within a common ‘DR technology cluster’; (b) deliver targeted support for adoption of electric vehicles and other storage and automation technologies; (c) enable and support informed adoption of DR-enabling products and services through ‘smarter’ digital comparison tools (DCTs), data portability, and faster, simpler switching. The interdependency between components within this DR technology cluster delivers efficiency but also poses a risk that one delayed component (e.g., smart metering) will hold-up policy and industry support for other components. The urgency of decarbonisation goals makes it necessary to push forward as many of these elements as possible rather than the pace being set by the slowest.

Abstract The influence of gas atmosphere on the quantity and quality of tar obtained from thermochemical processing of an inertinite-rich South African coal is reported in this work. The effect of syngas and steam on the produced tar is addressed. Experiments were carried out in a fixed bed reactor at 30 bar and four gas atmospheres: N2, N2 + steam, H2/CO and H2/CO + steam. Tar yield increased when H2/CO was used instead of N2. Additionally, the introduction of steam to the reaction atmosphere together with N2 or H2/CO gave rise to higher tar yields than those obtained under dry N2 or H2/CO alone. Tar quality was evaluated by size exclusion chromatography, simulated distillation, gas chromatography–mass spectrometry and infrared spectrometry. Tar evolved under H2/CO atmosphere presented lighter molecular weight distribution than that obtained under N2 atmosphere. Moreover, the presence of steam in the reaction atmosphere gave rise to tars with slightly broader molecular weight distribution, shifted towards heavier species. Although all tars exhibited wide variety of aromatic compounds, tar from H2/CO experiment showed the largest degree of aromatization. The addition of steam to H2/CO atmosphere yielded tar with lower degree of aromatization and oxygenated groups. In contrast, tar with higher degree of aromatization and oxygen functional groups were obtained when steam was added to N2 atmosphere.

In spite of the benefits from thermal energy storage (TES) integration in dwellings, the penetration rate in Europe is 5%. Effective fiscal policies are necessary to accelerate deployment. However, there is currently no direct support for TES in buildings compared to support for electricity storage. This could be due to lack of evidence to support incentivisation. In this study, a novel systematic framework is developed to provide a case in support of TES incentivisation. The model determines the costs, CO2 emissions, dispatch strategy and sizes of technologies, and TES for a domestic user under policy neutral and policy intensive scenarios. The model is applied to different building types in the UK. The model is applied to a case study for a detached dwelling in the UK (floor area of 122 m2), where heat demand is satisfied by a boiler and electricity imported from the grid. Results show that under a policy neutral scenario, integrating a micro-Combined Heat and Power (CHP) reduces the primary energy demand by 11%, CO2 emissions by 21%, but with a 16 year payback. Additional benefits from TES integration can pay for the investment within the first 9 years, reducing to 3.5–6 years when the CO2 levy is accounted for. Under a policy intensive scenario (for example considering the Feed in Tariff (FIT)), primary energy demand and CO2 emissions reduce by 17 and 33% respectively with a 5 year payback. In this case, the additional benefits for TES integration can pay for the investment in TES within the first 2 years. The framework developed is a useful tool is determining the role TES in decarbonising domestic energy systems.