Biochar: a synthesis of its agronomic impact beyond carbon sequestration.

Biochar has been heralded as an amendment to revitalize degraded soils, improve soil carbon sequestration, increase agronomic productivity, and enter into future carbon trading markets. However, scientific and economic technicalties may limit the ability of biochar to consistently deliver on these expectations. Past research has demonstrated that biochar is part of the black carbon continuum with variable properties due to the net result of production (e.g., feedstock and pyrolysis conditions) and postproduction factors (storage or activation). Therefore, biochar is not a single entity but rather spans a wide range of black carbon forms. Biochar is black carbon, but not all black carbon is biochar. Agronomic benefits arising from biochar additions to degraded soils have been emphasized, but negligible and negative agronomic effects have also been reported. Fifty percent of the reviewed studies reported yield increases after black carbon or biochar additions, with the remainder of the studies reporting alarming decreases to no significant differences. Hardwood biochar (black carbon) produced by traditional methods (kilns or soil pits) possessed the most consistent yield increases when added to soils. The universality of this conclusion requires further evaluation due to the highly skewed feedstock preferences within existing studies. With global population expanding while the amount of arable land remains limited, restoring soil quality to nonproductive soils could be key to meeting future global food production, food security, and energy supplies; biochar may play a role in this endeavor. Biochar economics are often marginally viable and are tightly tied to the assumed duration of agronomic benefits. Further research is needed to determine the conditions under which biochar can provide economic and agronomic benefits and to elucidate the fundamental mechanisms responsible for these benefits.

Fractionation of whey protein isolate with supercritical carbon dioxide-process modeling and cost estimation.

An economical and environmentally friendly whey protein fractionation process was developed using supercritical carbon dioxide (sCO(2)) as an acid to produce enriched fractions of α-lactalbumin (α-LA) and ß-lactoglobulin (ß-LG) from a commercial whey protein isolate (WPI) containing 20% α-LA and 55% ß-LG, through selective precipitation of α-LA. Pilot-scale experiments were performed around the optimal parameter range (T = 60 to 65 °C, P = 8 to 31 MPa, C = 5 to 15% (w/w) WPI) to quantify the recovery rates of the individual proteins and the compositions of both fractions as a function of processing conditions. Mass balances were calculated in a process flow-sheet to design a large-scale, semi-continuous process model using SuperproDesigner® software. Total startup and production costs were estimated as a function of processing parameters, product yield and purity. Temperature, T, pressure, P, and concentration, C, showed conflicting effects on equipment costs and the individual precipitation rates of the two proteins, affecting the quantity, quality, and production cost of the fractions considerably. The highest α-LA purity, 61%, with 80% α-LA recovery in the solid fraction, was obtained at T = 60 °C, C = 5% WPI, P = 8.3 MPa, with a production cost of $8.65 per kilogram of WPI treated. The most profitable conditions resulted in 57%-pure α-LA, with 71% α-LA recovery in the solid fraction and 89% ß-LG recovery in the soluble fraction, and production cost of $5.43 per kilogram of WPI treated at T = 62 °C, C = 10% WPI and P = 5.5 MPa. The two fractions are ready-to-use, new food ingredients with a pH of 6.7 and contain no residual acid or chemical contaminants.

A process model to estimate biodiesel production costs.

'Biodiesel' is the name given to a renewable diesel fuel that is produced from fats and oils. It consists of the simple alkyl esters of fatty acids, most typically the methyl esters. We have developed a computer model to estimate the capital and operating costs of a moderately-sized industrial biodiesel production facility. The major process operations in the plant were continuous-process vegetable oil transesterification, and ester and glycerol recovery. The model was designed using contemporary process simulation software, and current reagent, equipment and supply costs, following current production practices. Crude, degummed soybean oil was specified as the feedstock. Annual production capacity of the plant was set at 37,854,118 l (10 x 10(6)gal). Facility construction costs were calculated to be US dollar 11.3 million. The largest contributors to the equipment cost, accounting for nearly one third of expenditures, were storage tanks to contain a 25 day capacity of feedstock and product. At a value of US dollar 0.52/kg (dollar 0.236/lb) for feedstock soybean oil, a biodiesel production cost of US dollar 0.53/l (dollar 2.00/gal) was predicted. The single greatest contributor to this value was the cost of the oil feedstock, which accounted for 88% of total estimated production costs. An analysis of the dependence of production costs on the cost of the feedstock indicated a direct linear relationship between the two, with a change of US dollar 0.020/l (dollar 0.075/gal) in product cost per US dollar 0.022/kg (dollar 0.01/lb) change in oil cost. Process economics included the recovery of coproduct glycerol generated during biodiesel production, and its sale into the commercial glycerol market as an 80% w/w aqueous solution, which reduced production costs by approximately 6%. The production cost of biodiesel was found to vary inversely and linearly with variations in the market value of glycerol, increasing by US dollar 0.0022/l (dollar 0.0085/gal) for every US dollar 0.022/kg (dollar 0.01/lb) reduction in glycerol value. The model is flexible in that it can be modified to calculate the effects on capital and production costs of changes in feedstock cost, changes in the type of feedstock employed, changes in the value of the glycerol coproduct, and changes in process chemistry and technology.

Drying kinetics of calcium caseinate.

Drying is a major component of the cost of making caseinate-based films. We determined the drying curves for making calcium caseinate/glycerol films at low and high relative humidity at 21-34 degrees C. The drying curves exhibited a very long constant rate period followed by a single falling rate period. Much of the drying was in the constant rate period and preceded the actual film formation. Normally, calcium caseinate solutions are dried from about 5% solids, but it was possible to start with a more concentrated solution, 10% solids, to avoid much of the constant rate period. The resulting films were equal to those prepared starting at high initial moisture. An estimate of the drying costs indicated it is much cheaper to start with the more concentrated solutions.

Protease increases fermentation rate and ethanol yield in dry-grind ethanol production.

The effects of acid protease and urea addition during the fermentation step were evaluated. The fermentations were also tested with and without the addition of urea to determine if protease altered the nitrogen requirements of the yeast. Results show that the addition of the protease had a statistically significant effect on the fermentation rate and yield. Fermentation rates and yields were improved with the addition of the protease over the corresponding controls without protease. Protease addition either with or with added urea resulted in a higher final ethanol yield than without the protease addition. Urea addition levels >1200 ppm of supplemental nitrogen inhibited ethanol production. The economic effects of the protease addition were evaluated by using process engineering and economic models developed at the Eastern Regional Research Center. The decrease in overall processing costs from protease addition was as high as $0.01/L (4 ¢/gal) of denatured ethanol produced.

Economic analysis of fuel ethanol production from winter hulled barley by the EDGE (Enhanced Dry Grind Enzymatic) process.

A process and cost model was developed for fuel ethanol production from winter barley based on the EDGE (Enhanced Dry Grind Enzymatic) process. In this process, in addition to ß-glucanases, which are added to reduce the viscosity of the mash, ß-glucosidase is also added to completely hydrolyze the oligomers obtained during the hydrolysis of ß-glucans to glucose. The model allows determination of capital costs, operating costs, and ethanol production cost for a plant producing 40 million gallons of denatured fuel ethanol annually. A sensitivity study was also performed to examine the effects of ß-glucosidase and barley costs on the final ethanol production cost. The results of this study clearly demonstrate the economic benefit of adding ß-glucosidase. Lower ethanol production cost was obtained compared to that obtained without ß-glucosidase addition in all cases except one where highest ß-glucosidase cost allowance and lowest barley cost were used.

Enzymatic corn wet milling: engineering process and cost model.

BACKGROUND: Enzymatic corn wet milling (E-milling) is a process derived from conventional wet milling for the recovery and purification of starch and co-products using proteases to eliminate the need for sulfites and decrease the steeping time. In 2006, the total starch production in USA by conventional wet milling equaled 23 billion kilograms, including modified starches and starches used for sweeteners and ethanol production 1. Process engineering and cost models for an E-milling process have been developed for a processing plant with a capacity of 2.54 million kg of corn per day (100,000 bu/day). These models are based on the previously published models for a traditional wet milling plant with the same capacity. The E-milling process includes grain cleaning, pretreatment, enzymatic treatment, germ separation and recovery, fiber separation and recovery, gluten separation and recovery and starch separation. Information for the development of the conventional models was obtained from a variety of technical sources including commercial wet milling companies, industry experts and equipment suppliers. Additional information for the present models was obtained from our own experience with the development of the E-milling process and trials in the laboratory and at the pilot plant scale. The models were developed using process and cost simulation software (SuperPro Designer) and include processing information such as composition and flow rates of the various process streams, descriptions of the various unit operations and detailed breakdowns of the operating and capital cost of the facility. RESULTS: Based on the information from the model, we can estimate the cost of production per kilogram of starch using the input prices for corn, enzyme and other wet milling co-products. The work presented here describes the E-milling process and compares the process, the operation and costs with the conventional process. CONCLUSION: The E-milling process was found to be cost competitive with the conventional process during periods of high corn feedstock costs since the enzymatic process enhances the yields of the products in a corn wet milling process. This model is available upon request from the authors for educational, research and non-commercial uses.