Coal and Biomass to Liquid Transportation Fuels: Process Synthesis and Global Optimization Strategies

Progress in thermochemical conversion of aquatic weeds in shellfish aquaculture for biofuel generation: Technical and economic perspectives

Rapid growth of aquatic weeds in treatment pond poses undesirable challenge to shellfish aquaculture, requiring the farmers to dispose these weeds on a regular basis. This article reviews the potential and application of various aquatic weeds for generation of biofuels using recent thermochemical technologies (torrefaction, hydrothermal carbonization/liquefaction, pyrolysis, gasification). The influence of key operational parameters for optimising the aquatic weed conversion efficiency was discussed, including the advantages, drawbacks and techno-economic aspects of the thermochemical technologies, and their viability for large-scale application. Via extensive study in small and large scale operation, and the economic benefits derived, pyrolysis is identified as a promising thermochemical technology for aquatic weed conversion. The perspectives, challenges and future directions in thermochemical conversion of aquatic weeds to biofuels were also reviewed. This review provides useful information to promote circular economy by integrating shellfish aquaculture with thermochemical biorefinery of aquatic weeds rather than disposing them in landfills.

Accelerating Manufacturing for Biomass Conversion via Integrated Process and Bench Digitalization: A Perspective

We present a perspective for accelerating biomass manufacturing via digitalization. We summarize the challenges for manufacturing and identify areas where digitalization can help. A profound potential in using lignocellulosic biomass...

Kinetic Study of Product Distribution Using Various Data-Driven and Statistical Models for Fischer-Tropsch Synthesis

Three modeling techniques, namely, a radial basis function neural network (RBFNN), a comprehensive kinetic with genetic algorithm (CKGA), and a response surface methodology (RSM), were used to study the kinetics of Fischer-Tropsch (FT) synthesis. Using a 29 × 37 (4 independent process parameters as inputs and corresponding 36 responses as outputs) matrix with total 1073 data sets for data training through RBFNN, the established model is capable of predicting hydrocarbon product distribution i.e., the paraffin formation rate (C₂-C₁₅) and the olefin to paraffin ratio (OPR) within acceptable uncertainties. With additional validation data sets (15 × 36 matrix with total 540 data sets), the uncertainties of using three different models were compared and the outcomes were: RBFNN (±5% uncertainties), RSM (±10% uncertainties), and CKGA (±30% uncertainties), respectively. A new effective strategy for kinetic study of the complex FT synthesis is proposed: RBFNN is used for data matrix generation with a limited number of experimental data sets (due to its fast converge and less computation time), CKGA is used for mechanism selections by the Langmuir-Hinshelwood-Hougen-Watson (LHHW) approach using a genetic algorithm to find out potential reaction pathways, and RSM is used for statistical analysis of the investigated data matrix (generated from RBFNN through central composite design) upon responses and subsequent singular/multiple optimizations. The proposed strategy is a very useful and practical tool in process engineering design and practice for the product distribution during FT synthesis.

A framework to predict the price of energy for the end-users with applications to monetary and energy policies

Energy affects every single individual and entity in the world. Therefore, it is crucial to precisely quantify the “price of energy” and study how it evolves through time, through major political and social events, and through changes in energy and monetary policies. Here, we develop a predictive framework, an index to calculate the average price of energy in the United States. The complex energy landscape is thoroughly analysed to accurately determine the two key factors of this framework: the total demand of the energy products directed to the end-use sectors, and the corresponding price of each product. A rolling horizon predictive methodology is introduced to estimate future energy demands, with excellent predictive capability, shown over a period of 174 months. The effectiveness of the framework is demonstrated by addressing two policy questions of significant public interest.

Global energy transformation requires quantifying the "price of energy" and studying its evolution. Here the authors present a predictive framework that calculates the average US price of energy, estimating future energy demands for up to four years with excellent accuracy, designing and optimizing energy and monetary policies.

Carburized cobalt catalyst for the Fischer–Tropsch synthesis

Catalysts with Co, Co3C, and Co2C phases are synthesized by controlling the carburization time. The catalyst with Co3C phase exhibits the highest activity in FTS reaction. DFT calculations reveal the high intrinsic activity of Co3C.

Assessing the formation of cobalt carbide and its catalytic performance under realistic reaction conditions and tuning product selectivity in a cobalt-based FTS reaction

The formation of cobalt carbide (Co₂C) and its catalytic behavior were systematically investigated using realistic reaction conditions.