Sina Rezvani

Techno-economic assessment of biofuel development by anaerobic digestion of European marine cold-water seaweeds

The techno-economic characteristics of macro-algae utilisation from European temperate zones was evaluated in a selected Anaerobic Digester (AD) using the chemical process modelling software ECLIPSE. The assessment covered the mass and energy balance of the entire process followed by the economic feasibility study, which included the total cost estimation, net present value calculation, and sensitivity analysis. The selected plant size corresponded to a community based AD of 1.6 MWth with a macro-algae feed rate of 8.64 tonnes per day (dry basis). The produced biogas was utilised in a combined heat and power plant generating 237 kWenet electricity and 367 kWth heat. The breakeven electricity-selling price in this study was estimated at around €120/MWh. On the ground of different national and regional policies, this study did not account for any government incentives. However, different support mechanisms such as Feed-in-Tariffs or Renewable Obligation Certificates can significantly improve the project viability.

A hybrid system with multivariate data validation and case base reasoning for an efficient and realistic product formulation

This applied research paper presents a novel hybrid system, which provides a systematic approach for an efficient and realistic case retrieval, retention and testing of product formulations. The underlying idea is to build a case library of practical and viable product formulations with consistent quality patterns, flexible process attributes and constituent proportions. To avoid the storage of non-representative and unrealistic cases within the case library, a strict multivariate validation method has been imposed on the system. The input formulation, whether it be a single suggestion on product formulation as a query, an optimized case or a collection of tests, is validated against the most similar formulation cluster in the case library determined through the Principal Component Similarity factor and Mahalanobis distance. T2 and Q-statistics as multivariate data validation methods are employed to determine whether the input formulations match the most similar cluster of datasets in the case library. The synergistic use of univariate control charts and the graphical plot of variable relations between the input formulation and the most similar case provide information on variables, which cause a mismatch. If the value of the culprit variable cannot be rectified to match the dataset in the case library, the new input formulation can only be retained after an empirical validation in the main manufacturing area.

Integration and Performance Assessment of Hydrogen Fired Combined Cycle Power Plant With PSA Process for CO2 Capture in a Pre-Combustion IGCC Power Plant

The Pre-combustion IGCC Power Generation technology presents novel challenges in terms of gas turbine operation. The hydrogen-rich gaseous fuel has much higher heating value than conventional syngas. In a state of the art precombustion IGCC power plant, the fuel utilised in a gas turbine is generated on-site by a coal gasification unit followed by gas clean-up, water-gas (sour) shift reaction and CO2 sequestration. The Pressure Swing Adsorption (PSA) process is techno-economically promising option for separation of hydrogen from the syngas. However, the PSA process is inherently transient in nature. Performance of PSA process is dependent on the process configuration and various devised different PSA process configurations and reported the purity and recovery rate of hydrogen and carbon dioxide rich product gas streams. However, these process configurations published in literature are not directly adaptable to the state of the art pre-combustion IGCC power plant due to variations in the feed composition and condition. Moreover, the current research works have not addressed the impact of varying amount of hydrogen recovery on the power generation characteristics. Two important configurations of the PSA process are identified and their CAPEX is estimated.Copyright

Power generation from biomass in a small CFBC plant compared with biomass co-fired with coal in a large CFBC

SYNOPSIS Biomass is one of the renewable energy sources which is not intermittent, location-dependent or very difficult to store. If grown sustainably, biomass can be considered to be CO2 neutral. The use of biomass for power generation is also considered to be important in increasing the electricity output from renewable energy sources. However, power plants dedicated to the use of biomass fuel are not in widespread use and the acceptance of this fuel and development of the infrastructure for biomass production and transportation remain in their infancy. If small ratios of biomass can be co-fired with coal in large-scale conventional power plants, without significant technical, environmental or economic penalties, it could lead to a greater demand for biomass and so stimulate the industry. In this study a 80 MWth Circulating Fluidised Bed Combustion (CFBC) plant, fuelled by biomass only, and a large-scale 1000 MWth CFBC, co-fired with coal and 8% biomass, and the same large CFBC system, fired only with coal, are modelled using the ECLIPSE process simulation package and their technical, environmental and economic properties analysed and compared. The co-firing of biomass with coal was found to have little effect on the large-scale CFBC system, when a small ratio of biomass is used. The large scale system was found to have higher efficiency, lower CO2 emissions and lower breakeven electricity selling price than the small biomass-fuelled CFBC. Co-firing of biomass with coal could be a promising way of promoting the production, use and acceptance of biomass as a fuel in electricity generation.

An intelligent approach to generating improved product formulation for an optimal manufacturing route

Case Based Reasoning System (CBR) is an efficient and straightforward approach for recommending desirable product formulations. In response to a changeable market situation, it is vital to adapt or generate enhanced and more efficient product formulations and manufacturing routes. Due to the lack of variability and indefinite data distribution in many case libraries, however, this technique can be very limited and is inadequate for creating improved formulations. The proposed approach represents an intelligent way to overcome this shortcoming. Following a carefully premeditated Design of Experiment (DOE), a synergistic use of data noise removal, modeling and optimization techniques were utilized to develop favorable product formulations. After a multivariate validation, these along with DOE based formulations are retained in the CBR library for later use.

An Extended Multivariate Data Visualization Approach for Interactive Feature Extraction from Manufacturing Data

Awareness about interconnectivities and interactions among parameters is vital for the identification of the optimal manufacturing routes and economic factors within a manufacturing system. Within this context, multidimensional data projection methods, Principal Component Mapping (PCM) and Sammons Mapping, have been scrutinized for visualizing multivariate interaction patterns. As a new approach, these techniques were employed in such a way that interactive multi-layer maps could be created. Each layer within the generated map matches to a specific attribute and characteristic of the dataset. Individual layers within the map can be interactively selected and superimposed to show multiple and partial interactions.