Integrated Biomass Gasification with Catalytic Partial Oxidation for Selective Tar Conversion

Biomass gasification is a flexible and efficient way of utilizing widely available domestic renewable resources. Syngas from biomass has the potential for biofuels production, which will enhance energy security and environmental benefits. Additionally, with the successful development of low Btu fuel engines (e.g. GE Jenbacher engines), syngas from biomass can be efficiently used for power/heat co-generation. However, biomass gasification has not been widely commercialized because of a number of technical/economic issues related to gasifier design and syngas cleanup. Biomass gasification, due to its scale limitation, cannot afford to use pure oxygen as the gasification agent that used in coal gasification. Because, it uses air instead of oxygen, the biomass gasification temperature is much lower than well-understood coal gasification. The low temperature leads to a lot of tar formation and the tar can gum up the downstream equipment. Thus, the biomass gasification tar removal is a critical technology challenge for all types of biomass gasifiers. This USDA/DOE funded program (award number: DE-FG36-O8GO18085) aims to develop an advanced catalytic tar conversion system that can economically and efficiently convert tar into useful light gases (such as syngas) for downstream fuel synthesis or power generation. This program has been executed by GE Global Research in Irvine, CA, in collaboration with Professor Lanny Schmidt's group at the University of Minnesota (UoMn). Biomass gasification produces a raw syngas stream containing H2, CO, CO2, H2O, CH4 and other hydrocarbons, tars, char, and ash. Tars are defined as organic compounds that are condensable at room temperature and are assumed to be largely aromatic. Downstream units in biomass gasification such as gas engine, turbine or fuel synthesis reactors require stringent control in syngas quality, especially tar content to avoid plugging (gum) of downstream equipment. Tar- and ash-free syngas streams are a critical requirement for commercial deployment of biomass-based power/heat co-generation and biofuels production. There are several commonly used syngas clean-up technologies: (1) Syngas cooling and water scrubbing has been commercially proven but efficiency is low and it is only effective at small scales. This route is accompanied with troublesome wastewater treatment. (2) The tar filtration method requires frequent filter replacement and solid residue treatment, leading to high operation and capital costs. (3) Thermal destruction typically operates at temperatures higher than 1000oC. It has slow kinetics and potential soot formation issues. The system is expensive and materials are not reliable at high temperatures. (4) In-bed cracking catalysts show rapid deactivation, with durability to be demonstrated. (5) External catalytic cracking or steam reforming has low thermal efficiency and is faced with problematic catalyst coking. Under this program, catalytic partial oxidation (CPO) is being evaluated for syngas tar clean-up in biomass gasification. The CPO reaction is exothermic, implying that no external heat is needed and the system is of high thermal efficiency. CPO is capable of processing large gas volume, indicating a very compact catalyst bed and a low reactor cost. Instead of traditional physical removal of tar, the CPO concept converts tar into useful light gases (eg. CO, H2, CH4). This eliminates waste treatment and disposal requirements. All those advantages make the CPO catalytic tar conversion system a viable solution for biomass gasification downstream gas clean-up. This program was conducted from October 1 2008 to February 28 2011 and divided into five major tasks. - Task A: Perform conceptual design and conduct preliminary system and economic analysis (Q1 2009 ~ Q2 2009) - Task B: Biomass gasification tests, product characterization, and CPO tar conversion catalyst preparation. This task will be conducted after completing process design and system economics analysis. Major milestones include identification of syngas cleaning requirements for proposed system design, identification and selection of tar compounds and 2 mixtures for use in CPO tests, and preparation of CPO catalysts for validation. (Q3 2009 ~ Q4 2009) - Task C: Test CPO with biomass gasification product gas. Optimize CPO performance with selected tar compounds. Optimize CPO performance with multi-component mixtures. Milestones include optimizing CPO catalysts design, collecting CPO experimental data for next stage kinetic modeling and understanding the effect of relative reactivities on ultimate tar conversion and syngas yields. (Q1 2010 ~ Q3 2010) - Task D: Develop tar CPO kinetic model with CPO kinetic model and modeling results as deliverables. (Q3 2010 ~ Q2 2011) - Task E: Project management and reporting. Milestone: Quarterly reports and presentations, final report, work presented at national technical conferences (Q1 2009 ~ Q2 2011) At the beginning of the program, IP landscaping was conducted to understand the operation of various types of biomass gasifiers, their unique syngas/tar compositions and potential tar mitigation options using the catalytic partial oxidation technology. A process simulation model was developed to quantify the system performance and economics impact of CPO tar removal technology. Biomass gasification product compositions used for performance evaluation tests were identified after literature review and system modeling. A reaction system for tar conversion tests was designed, constructed, with each individual component shaken-down in 2009. In parallel, University of Minnesota built a lab-scale unit and evaluated the tar removal performance using catalytic reforming. Benzene was used as the surrogate compound. The biomass gasification raw syngas composition was provided by GE through system studies. In 2010, GE selected different tar compounds and evaluated the tar removal effectiveness of the CPO catalyst. The catalytic performance was evaluated under different operating conditions, including catalyst geometry, S/C ratio, O/C ratio, GHSV, and N2 dilution. An understanding of how to optimize catalytic tar removal efficiency by varying operating conditions has been developed. GE collaborated with UoMn in examining inorganic impurities effects. Catalysts were pre-impregnated with inorganic impurities commonly present in biomass gasification syngas, including Si, Ca, Mg, Na, K, P and S. UoMn performed catalyst characterization and has acquired fundamental understandings of impurities effect on catalytic tar removal. Based on experimental data and the proposed reaction pathway, GE constructed a model to predict kinetic performance for biomass gasification tar cleanup process. Experimental data (eg. tar conversion, reactor inlet and outlet temperatures, product distribution) at different operating conditions were used to validate the model. A good fit between model predictions and experimental data was found. This model will be a valuable tool in designing the tar removal reactor and identifying appropriate operating conditions. We attended the 2011 DOE Biomass Program Thermochemical Platform Review held in Denver, CO from February 16 to 18 and received very positive comments from the review panel. Further, syngas utility and biomass to power/fuel companies expressed strong interest in our tar removal technology

Improving Conversational Recommender System via Contextual and Time-Aware Modeling with Less Domain-Specific Knowledge

Conversational Recommender Systems (CRS) has become an emerging research topic seeking to perform recommendations through interactive conversations, which generally consist of generation and recommendation modules. Prior work on CRS tends to incorporate more external and domain-specific knowledge like item reviews to enhance performance. Despite the fact that the collection and annotation of the external domain-specific information needs much human effort and degenerates the generalizability, too much extra knowledge introduces more difficulty to balance among them. Therefore, we propose to fully discover and extract internal knowledge from the context. We capture both entity-level and contextual-level representations to jointly model user preferences for the recommendation, where a time-aware attention is designed to emphasize the recently appeared items in entity-level representations. We further use the pre-trained BART to initialize the generation module to alleviate the data scarcity and enhance the context modeling. In addition to conducting experiments on a popular dataset (ReDial), we also include a multi-domain dataset (OpenDialKG) to show the effectiveness of our model. Experiments on both datasets show that our model achieves better performance on most evaluation metrics with less external knowledge and generalizes well to other domains. Additional analyses on the recommendation and generation tasks demonstrate the effectiveness of our model in different scenarios

Innovative delivery systems for epicutaneous immunotherapy

Allergen-specific immunotherapy (AIT) describes the establishment of peripheral tolerance through repeated allergen exposure, which qualifies as the only curative treatment for allergic diseases. Although conventional subcutaneous immunotherapy (SCIT) and sublingual immunotherapy (SLIT) have been approved to treat respiratory allergies clinically, the progress made is far from satisfactory. Epicutaneous immunotherapy (EPIT) exploits the skin’s immune properties to modulate immunological response, which is emerging as a promising alternative and has shown effectiveness in many preclinical and clinical studies for both respiratory and food allergies. It is worth noting that the stratum corneum (SC) barrier impedes the effective delivery of allergens, while disrupting the SC layer excessively often triggers unexpected Th2 immune responses. This work aims to comprehend the immunological mechanisms of EPIT, and summarize the innovative system for sufficient delivery of allergens as well as tolerogenic adjuvants. Finally, the safety, acceptability, and cost-effectiveness of these innovative delivery systems are discussed, which directs the development of future immunotherapies with all desirable characteristics

KERMIT: Knowledge Graph Completion of Enhanced Relation Modeling with Inverse Transformation

Knowledge graph completion is a task that revolves around filling in missing triples based on the information available in a knowledge graph. Among the current studies, text-based methods complete the task by utilizing textual descriptions of triples. However, this modeling approach may encounter limitations, particularly when the description fails to accurately and adequately express the intended meaning. To overcome these challenges, we propose the augmentation of data through two additional mechanisms. Firstly, we employ ChatGPT as an external knowledge base to generate coherent descriptions to bridge the semantic gap between the queries and answers. Secondly, we leverage inverse relations to create a symmetric graph, thereby creating extra labeling and providing supplementary information for link prediction. This approach offers additional insights into the relationships between entities. Through these efforts, we have observed significant improvements in knowledge graph completion, as these mechanisms enhance the richness and diversity of the available data, leading to more accurate results

Integrated Biomass Gasification with Catalytic Partial Oxidation for Selective Tar Conversion

Biomass gasification is a flexible and efficient way of utilizing widely available domestic renewable resources. Syngas from biomass has the potential for biofuels production, which will enhance energy security and environmental benefits. Additionally, with the successful development of low Btu fuel engines (e.g. GE Jenbacher engines), syngas from biomass can be efficiently used for power/heat co-generation. However, biomass gasification has not been widely commercialized because of a number of technical/economic issues related to gasifier design and syngas cleanup. Biomass gasification, due to its scale limitation, cannot afford to use pure oxygen as the gasification agent that used in coal gasification. Because, it uses air instead of oxygen, the biomass gasification temperature is much lower than well-understood coal gasification. The low temperature leads to a lot of tar formation and the tar can gum up the downstream equipment. Thus, the biomass gasification tar removal is a critical technology challenge for all types of biomass gasifiers. This USDA/DOE funded program (award number: DE-FG36-O8GO18085) aims to develop an advanced catalytic tar conversion system that can economically and efficiently convert tar into useful light gases (such as syngas) for downstream fuel synthesis or power generation. This program has been executed by GE Global Research in Irvine, CA, in collaboration with Professor Lanny Schmidt's group at the University of Minnesota (UoMn). Biomass gasification produces a raw syngas stream containing H2, CO, CO2, H2O, CH4 and other hydrocarbons, tars, char, and ash. Tars are defined as organic compounds that are condensable at room temperature and are assumed to be largely aromatic. Downstream units in biomass gasification such as gas engine, turbine or fuel synthesis reactors require stringent control in syngas quality, especially tar content to avoid plugging (gum) of downstream equipment. Tar- and ash-free syngas streams are a critical requirement for commercial deployment of biomass-based power/heat co-generation and biofuels production. There are several commonly used syngas clean-up technologies: (1) Syngas cooling and water scrubbing has been commercially proven but efficiency is low and it is only effective at small scales. This route is accompanied with troublesome wastewater treatment. (2) The tar filtration method requires frequent filter replacement and solid residue treatment, leading to high operation and capital costs. (3) Thermal destruction typically operates at temperatures higher than 1000oC. It has slow kinetics and potential soot formation issues. The system is expensive and materials are not reliable at high temperatures. (4) In-bed cracking catalysts show rapid deactivation, with durability to be demonstrated. (5) External catalytic cracking or steam reforming has low thermal efficiency and is faced with problematic catalyst coking. Under this program, catalytic partial oxidation (CPO) is being evaluated for syngas tar clean-up in biomass gasification. The CPO reaction is exothermic, implying that no external heat is needed and the system is of high thermal efficiency. CPO is capable of processing large gas volume, indicating a very compact catalyst bed and a low reactor cost. Instead of traditional physical removal of tar, the CPO concept converts tar into useful light gases (eg. CO, H2, CH4). This eliminates waste treatment and disposal requirements. All those advantages make the CPO catalytic tar conversion system a viable solution for biomass gasification downstream gas clean-up. This program was conducted from October 1 2008 to February 28 2011 and divided into five major tasks. - Task A: Perform conceptual design and conduct preliminary system and economic analysis (Q1 2009 ~ Q2 2009) - Task B: Biomass gasification tests, product characterization, and CPO tar conversion catalyst preparation. This task will be conducted after completing process design and system economics analysis. Major milestones include identification of syngas cleaning requirements for proposed system design, identification and selection of tar compounds and 2 mixtures for use in CPO tests, and preparation of CPO catalysts for validation. (Q3 2009 ~ Q4 2009) - Task C: Test CPO with biomass gasification product gas. Optimize CPO performance with selected tar compounds. Optimize CPO performance with multi-component mixtures. Milestones include optimizing CPO catalysts design, collecting CPO experimental data for next stage kinetic modeling and understanding the effect of relative reactivities on ultimate tar conversion and syngas yields. (Q1 2010 ~ Q3 2010) - Task D: Develop tar CPO kinetic model with CPO kinetic model and modeling results as deliverables. (Q3 2010 ~ Q2 2011) - Task E: Project management and reporting. Milestone: Quarterly reports and presentations, final report, work presented at national technical conferences (Q1 2009 ~ Q2 2011) At the beginning of the program, IP landscaping was conducted to understand the operation of various types of biomass gasifiers, their unique syngas/tar compositions and potential tar mitigation options using the catalytic partial oxidation technology. A process simulation model was developed to quantify the system performance and economics impact of CPO tar removal technology. Biomass gasification product compositions used for performance evaluation tests were identified after literature review and system modeling. A reaction system for tar conversion tests was designed, constructed, with each individual component shaken-down in 2009. In parallel, University of Minnesota built a lab-scale unit and evaluated the tar removal performance using catalytic reforming. Benzene was used as the surrogate compound. The biomass gasification raw syngas composition was provided by GE through system studies. In 2010, GE selected different tar compounds and evaluated the tar removal effectiveness of the CPO catalyst. The catalytic performance was evaluated under different operating conditions, including catalyst geometry, S/C ratio, O/C ratio, GHSV, and N2 dilution. An understanding of how to optimize catalytic tar removal efficiency by varying operating conditions has been developed. GE collaborated with UoMn in examining inorganic impurities effects. Catalysts were pre-impregnated with inorganic impurities commonly present in biomass gasification syngas, including Si, Ca, Mg, Na, K, P and S. UoMn performed catalyst characterization and has acquired fundamental understandings of impurities effect on catalytic tar removal. Based on experimental data and the proposed reaction pathway, GE constructed a model to predict kinetic performance for biomass gasification tar cleanup process. Experimental data (eg. tar conversion, reactor inlet and outlet temperatures, product distribution) at different operating conditions were used to validate the model. A good fit between model predictions and experimental data was found. This model will be a valuable tool in designing the tar removal reactor and identifying appropriate operating conditions. We attended the 2011 DOE Biomass Program Thermochemical Platform Review held in Denver, CO from February 16 to 18 and received very positive comments from the review panel. Further, syngas utility and biomass to power/fuel companies expressed strong interest in our tar removal technology

A multi-objective evolutionary algorithm based on coordinate transformation

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.In this paper, a novel multiobjective evolutionary algorithm (MOEA/CT) is proposed to better manage convergence and distribution of solutions when MOEAs are used for solving multiobjective optimization problems. The coordinate transformation strategy, an external archive update strategy, and a diversity maintenance strategy are proposed in MOEA/CT. The coordinate transformation strategy in the objective space is designed to find more efficient solutions that can accelerate the convergence process. Based on the coordinate transformation strategy, a novel update strategy and diversity maintenance approach for selecting nondominated solutions from the external archive set are integrated in MOEA/CT for getting better distribution of the solutions. The proposed MOEA/CT is compared with eight state-of-art algorithms on six biobjective and seven tri-objective test problems. In terms of four performance metrics, the comparative experimental results demonstrate that MOEA/CT outperforms the other eight competitors and it can achieve solutions with better distribution and better convergence to the Pareto front. In addition, parameter sensitivity analysis is provided to investigate the effect of a key parameter in MOEA/CT; the proposed three strategies are also studied individually to investigate their contribution to MOEA/CT; the performance analysis along with the capacity of external archive is given to clearly make the influence in MOEA/CT; finally, the scalability performance of MOEA/CT is investigated and compared with five notable many-objective evolutionary algorithms on the DTLZ and WFG test suites with 5, 8, 10, and 15 objectives

Predictors of lung adenocarcinoma with leptomeningeal metastases: A 2022 targeted-therapy-assisted molGPA model

Objective: To explore prognostic indicators of lung adenocarcinoma with leptomeningeal metastases (LM) and provide an updated graded prognostic assessment model integrated with molecular alterations (molGPA). Methods: A cohort of 162 patients was enrolled from 202 patients with lung adenocarcinoma and LM. By randomly splitting data into the training (80%) and validation (20%) sets, the Cox regression and random survival forest methods were used on the training set to identify statistically significant variables and construct a prognostic model. The C-index of the model was calculated and compared with that of previous molGPA models. Results: The Cox regression and random forest models both identified four variables, which included KPS, LANO neurological assessment, TKI therapy line, and controlled primary tumor, as statistically significant predictors. A novel targeted-therapy-assisted molGPA model (2022) using the above four prognostic factors was developed to predict LM of lung adenocarcinoma. The C-indices of this prognostic model in the training and validation sets were higher than those of the lung-molGPA (2017) and molGPA (2019) models. Conclusions: The 2022 molGPA model, a substantial update of previous molGPA models with better prediction performance, may be useful in clinical decision making and stratification of future clinical trials

Single-cell RNA sequencing reveals cell type-specific immune regulation associated with human neuromyelitis optica spectrum disorder

IntroductionOne rare type of autoimmune disease is called neuromyelitis optica spectrum disorder (NMOSD) and the peripheral immune characteristics of NMOSD remain unclear.MethodsHere, single-cell RNA sequencing (scRNA-seq) is used to characterize peripheral blood mononuclear cells from individuals with NMOSD.ResultsThe differentiation and activation of lymphocytes, expansion of myeloid cells, and an excessive inflammatory response in innate immunity are observed. Flow cytometry analyses confirm a significant increase in the percentage of plasma cells among B cells in NMOSD. NMOSD patients exhibit an elevated percentage of CD8+ T cells within the T cell population. Oligoclonal expansions of B cell receptors are observed after therapy. Additionally, individuals with NMOSD exhibit elevated expression of CXCL8, IL7, IL18, TNFSF13, IFNG, and NLRP3.DiscussionPeripheral immune response high-dimensional single-cell profiling identifies immune cell subsets specific to a certain disease and identifies possible new targets for NMOSD