Conversion of Non-Homogeneous Biomass to Ultraclean Syngas and Catalytic Conversion to Ethanol
Bibliographic record
Abstract
Reducing greenhouse gas emissions, rising energy prices and security of supply are reasons that justify the development of biofuels.However, food prices recorded in 2007 and 2008 affected more than 100 million of people that became undernourished worldwide (Rastoin, 2008) The food crisis has been caused by several factors: underinvestment in agriculture, heavy speculation on agricultural commodities and competition of biofuels vs. food.It is estimated that by 2050, it will be essential to increase by 50% the food production to support the 9 billion people living on the planet (Rastoin, 2008).Recycling the carbon from residual waste to produce biofuels is one of the challenges of this new century.Several companies have been developing technologies that are able to transform residual streams into syngas, which is subsequently converted into alcohols."Green" ethanol plays an important role in reducing dependency toward petroleum and providing environmental benefit, through its role in the fuel additive market.Ethanol is an oxygenate and also serves as an octane enhancer.The waste-to-syngas approach is an alternative to avoid the controversy food vs. fuel whilst reducing landfills and increasing carbon recuperation.Using this approach, yields of ethanol produced are above 350 liters/dry tonne of feedstock entering the gasifier (Enerkem's technology is taken as example).Residual heat, also a product of the process, is used in the process itself and, as well, it can be used for outside heating or cooling.Enerkem Inc. is moving the technology from bench scale, to pilot, to demo to commercial implementation (a 12,500 kg/h of sorted and biotreated urban waste, is being constructed in Edmonton, Alberta).Economics of the process are favorable at the above commercial capacity, given the modular construction of the plant, reasonable operational costs and a tipping fee for the residue going into the gasifier.The first part of this chapter will present feedstock preparation, gasification and gas conditioning.The characteristics of the heterogeneous feedstock will determine its performance during gasification for syngas production whose composition has the appropriate H 2 /CO ratio for downstream synthesis.The second part of the chapter will be directed at the methanol synthesis in a three-phase reactor using syngas.The third and last part of the chapter will focus on the catalytic steps to convert methanol into bio-ethanol. www.intechopen.comBiofuel's Engineering Process Technology 334 Synthesis gas (syngas) production by gasification Characteristics and composition of heterogeneous wastes as feedstockBiomass is defined as an organic material derived from plants or animals that contain potential chemical energy; for example wood, which was the first fire source used by mankind, and which is still used today by population for cooking and heating.At world scale, biomass is now the fourth largest energy source, but it has the capacity to become the first.Photosynthesis can store up to 5-8 times more energy in biomass annually than the actual world energy consumption (Prins et al., 2005).The basic reaction of photosynthesis is as follow: carbon dioxide and water are converted to glucose and oxygen, an endothermic process for which the energy is supplied by photons.Examples of biomass are residues from agriculture or from the forest industry such as branches, straw, stalks, saw dust, etc.An important example of residual agricultural biomass is related to the ethanol production from sugar cane in Brazil which produces 280 kg of residual bagasse at 50% of dry solids.Lignocellulosic materials can be collected and recovered because this material has some energetic content (Ballerini and Alazard-Toux, 2006), however, leaving a part of this material on place is imperative since it keeps the soil fertile.It is important for governments and citizens to realize that hydrocarbon-based waste material is another source of energy that should be taken advantage on.Waste can be solid or liquid form.It can be land filled, incinerated or converted.Municipal solid waste used electrical transmission poles and railroad ties treated with creosote, sludge from wastewater treatment and pulp and paper industries, wood from construction and demolition operations which contains paints and resins, etc., are all materials that contain carbon that can be valorized in bio-refineries such as the one Enerkem is constructing (2011) in Edmonton, AB.Assessment of residual biomass or Municipal Solid Waste (MSW) as feedstock to produce bio-ethanol requires a basic understanding of feedstock composition and of the specific properties that dictates its performance as feed in the gasifier.The most important are: moisture content, ash content, volatile matter content, elemental composition and heating value.The moisture content of biomass is the quantity of water in the material, expressed as percentage of material weight.This weight can be referred to on a wet basis or on a dry basis.If the moisture content is determined on a "wet" basis, the water's weight is expressed as a percentage of the sum of the weight of the water, ash, and dry-ash free matter.It is sometimes necessary to dry the feedstock to a certain level in order to maximize the gasification reaction.Indeed, more moisture is transferred by a higher consumption of oxygen in order to keep the ideal temperature in the gasifier.Temperature of gasification is crucial on the process efficiency.An optimum exists with just the right amount of oxygen needed to perform completely the gasification reaction.This represents a temperature of about 660°C for biomass with 20 % of moisture and about 695°C for biomass with 10 % of moisture (Prins et al., 2005).If more oxygen is added, formation of carbon dioxide will increase and gasification efficiency will drop; the heating value of the synthesis gas will thus decrease (van der Drift et al., 2001).However, not enough oxygen will promote reduction of carbon leading to an increase of methane formation.The inorganic component (ash content) can be expressed the same way as the moisture content.In general, the ash content is expressed on a dry basis.Both total ash content and chemical composition are both important in regards of the gasification process.The www.intechopen.com
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".