Development of a techno-economic energy model for low carbon business parks

To mitigate climate destabilisation, global emissions of human-induced greenhouse gases urgently need to be reduced, to be nearly zeroed at the end of the century. Clear targets are set at European level for the reduction of greenhouse gas emissions and primary energy consumption and for the integration of renewable energy. Carbon dioxide emissions from fossil fuel combustion in the industry and energy sectors account for a major share of greenhouse gas emissions. Hence, a low carbon shift in industrial and business park energy systems is called for. Low carbon business parks minimise energy-related carbon dioxide emissions by enhanced energy efficiency, heat recovery in and between companies, maximal exploitation of local renewable energy production, and energy storage, combined in a collective energy system. Moreover, companies with complementary energy profiles are clustered to exploit energy synergies. The design of low carbon energy systems is facilitated using the holistic approach of techno-economic energy models. These models take into account the complex interactions between the components of an energy system and assist in determining an optimal trade-off between energetic, economic and environmental performances. In this work, existing energy model classifications are scanned for adequate model characteristics and accordingly, a confined number of energy models are selected and described. Subsequently, a practical categorisation is proposed, existing of energy system evolution, optimisation, simulation, accounting and integration models, while key model features are compared. Next, essential features for modelling energy systems at business park scale are identified: As a first key feature, a superstructure-based optimisation approach avoids the need for a priori decisions on the system’s configuration, since a mathematical algorithm automatically identifies the optimal configuration in a superstructure that embeds all feasible configurations. Secondly, the representation of time needs to incorporate sufficient temporal detail to capture important characteristics and peaks in time-varying energy demands, energy prices and operation conditions of energy conversion technologies. Thirdly, energy technologies need to be accurately represented at equipment unit level by incorporating part-load operation and investment cost subject to economy of scale in the model formulation. In addition, the benefits of installing multiple units per technology must be considered. A generic model formulation of technology models facilitates the introduction of new technology types. As a fourth important feature, the potential of thermodynamically feasible heat exchange between thermal processes needs to be exploited, while optimally integrating energy technologies to fulfil remaining thermal demands. For this purpose, thermal streams need to be represented by heat –temperature profiles. Moreover, restrictions to direct heat exchange between process streams need to be taken into account. Finally, the possibility for energy storage needs to be included to enhance the integration of non-dispatchable renewable energy technologies and to bridge any asynchrony between cooling and heating demands. Starting from these essential features, a techno-economic optimisation model (Syn-E-Sys), is developed customised for the design of low carbon energy systems on business park scale. The model comprises two sequential stages. In the first stage, heat recovery within the system is maximised, while energy supply and energy storage technologies are optimally integrated and designed to fulfil remaining energy requirements at minimum total annualised costs. Predefined variations in thermal and electrical energy demand and supply are taken into account, next to a carbon emission cap. At the same time, heat networks can be deployed to transfer heat between separate parts of the system. In the second stage, the model generates an optimal multi-period heat exchanger network enabling all required heat exchanges. Syn-E-Sys builds upon a multi-period energy integration model that can deal with restrictions in heat exchange. It is combined with a generic technology model, that features part-load operation as well as investment cost subject to economy of scale, and a generic energy storage model. The technology model can be manipulated to represent various thermal or electrical energy conversion technology units, and serves as a building block to model more complex technologies. The storage model covers electrical as well as thermal energy storage, taking into account the effect of hourly energy losses on the storage level, without increasing the number of time steps to be analysed. For this purpose, time sequence is introduced by dividing the year into a set of time slices and assigning them to a hierarchical time structure. In addition, a more complex model for storage of sensible heat is integrated, consisting of a stack of interconnected virtual tanks. To enable the optimisation of the number of units per technology in the energy system configuration, an automated superstructure expansion procedure is incorporated. Heat transfer unit envelope curves are calculated to facilitate the choice of appropriate temperature levels for heat networks. Heat networks that are embedded within this envelope, completely avoid the increase in energy requirements that would result from the heat exchange restrictions between separated parts of the energy system. Finally, the heat exchanger network is automatically generated using a multi-period stage-wise superstructure. Two problems inherent to the heat cascade formulation are encountered during model development. As a first issue, heat networks can form self-sustaining energy loops if their hot and cold streams are not completely embedded within the envelope. This phenomenon is referred to in this work as phantom heat. As a second issue, the heat cascade formulation does not prevent that a thermal storage releases its heat to a cooling technology. To demonstrate the specific features of Syn-E-Sys and its holistic approach towards the synthesis of low carbon energy systems, the model is applied to a generic case study and to a case study from literature. The generic case study is set up to demonstrate the design of an energy system including non-dispatchable renewable energy and energy storage, subject to a carbon emission cap. For this purpose, the year is subdivided into a set of empirically defined time slices that are connected to a hierarchical time structure composed of seasons, daytypes and intra-daily time segments. The results obtained by Syn-E-Sys show a complex interaction between energy supply, energy storage and energy import/export to fulfil energy demands, while keeping carbon emissions below the predefined cap. The model enables optimisation of the intra-annual charge pattern and the capacity of thermal and electrical storage. Moreover, an optimal heat exchanger network is automatically generated. In the second case study, heat recovery is optimised for a drying process in the paper industry. To avoid the energy penalty due to heat exchange restrictions between two separated process parts, heat transfer units need to be optimally integrated. Firstly, a simplified version of the original problem is set up in Syn-E-Sys and the obtained results correspond well to literature. Subsequently, the original problem is extended to demonstrate the optimal integration of heat transfer units in a multi-period situation. In conclusion, Syn-E-Sys facilitates optimal design of low carbon energy systems on business park scale, taking into account the complex time-varying interactions between thermal and electrical energy demand, supply and storage, while the potential for heat recovery is fully exploited

Techno-economic energy models for low carbon business parks

To mitigate climate change, global greenhouse gas emissions need to be reduced substantially. Industry and energy sector together are responsible for a major share of those emissions. Hence the development of low carbon business parks by maximising energy efficiency and changing to collective, renewable energy systems at local level holds a high reduction potential. Yet, there is no uniform approach to determine the optimal combination and operation of energy technologies composing such energy systems. However, techno-economic energy models, custom tailored for business parks, can offer a solution, as they identify the configuration and operation that provide an optimal trade-off between economic and environmental performances. However, models specifically developed for industrial park energy systems are not detected in literature, so identifying an existing model that can be adapted is an essential step. In this paper, energy model classifications are scanned for adequate model characteristics and accordingly, a confined number of models are selected and described. Subsequently, main model features are compared, a practical typology is proposed and applicability towards modelling industrial park energy systems is evaluated. Energy system evolution models offer the most perspective to compose a holistic, but simplified model, whereas advanced energy system integration models can adequately be employed to assess energy integration for business clusters up to entire industrial sites. Energy system simulation models, however, provide deeper insight in the system’s operation

Genetic landscape of congenital insensitivity to pain and hereditary sensory and autonomic neuropathies

Congenital insensitivity to pain (CIP) and hereditary sensory and autonomic neuropathies (HSAN) are clinically and genetically heterogeneous disorders exclusively or predominantly affecting the sensory and autonomic neurons. Due to the rarity of the diseases and findings based mainly on single case reports or small case series, knowledge about these disorders is limited. Here, we describe the molecular workup of a large international cohort of CIP/HSAN patients including patients from normally under-represented countries. We identify 80 previously unreported pathogenic or likely pathogenic variants in a total of 73 families in the >20 known CIP/HSAN-associated genes. The data expand the spectrum of disease-relevant alterations in CIP/HSAN, including novel variants in previously rarely recognized entities such as ATL3-, FLVCR1- and NGF-associated neuropathies and previously under-recognized mutation types such as larger deletions. In silico predictions, heterologous expression studies, segregation analyses and metabolic tests helped to overcome limitations of current variant classification schemes that often fail to categorize a variant as disease-related or benign. The study sheds light on the genetic causes and disease-relevant changes within individual genes in CIP/HSAN. This is becoming increasingly important with emerging clinical trials investigating subtype or gene-specific treatment strategies

31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two

Background The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd. Methods We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background. Results First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001). Conclusions In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival

Techno-economic optimisation models for low carbon business park energy systems

On low carbon business or industrial parks, energy-related carbon dioxide emissions are minimised by local renewable energy production, enhanced energy efficiency, and inter-firm heat exchange, combined in a collective energy system [1]. The optimal design of such systems is a complex task and requires the holistic approach of techno-economic energy models. However, no models custom-tailored for energy systems at business park scale are currently available. Therefore, a number of optimisation-based energy models are analysed and important qualitative features are identified. These features provide a basis for the development of a new optimization model for low carbon business park energy systems