Oladiran Fasina

Physiochemical properties of bio-oil produced at various temperatures from pine wood using an auger reactor.

A fast pyrolysis process produces a high yield of liquid (a.k.a. bio-oil) and has gained a lot of interest among various stakeholders. Nonetheless, some of the properties inherent by the bio-oil create significant challenges for its wider applications. Quality of the bio-oil and its yield are highly dependent on process parameters, such as temperature, feedstock, moisture content and residence time. In this study, the effect of temperature on bio-oil quality and its yield were examined using pine wood, an abundant biomass source in the southeastern part of the United States. Physical properties of bio-oil such as pH, water content, higher heating value, solid content and ash were analyzed and compared with a recently published ASTM standard. Bio-oil produced from pine wood using an auger reactor met specifications suggested by the ASTM standard. Thirty-two chemical compounds were analyzed. The study found that the concentration of phenol and its derivatives increased with the increase in pyrolysis temperature whereas the concentration of guaiacol and its derivatives decreased as the temperature increased. Concentration of acetic and other acids remained almost constant or increased with the increase in temperature although the pH value of the bio-oil decreased with the increase in temperature.

Viscosity and Specific Heat of Vegetable Oils as a Function of Temperature: 35°C to 180°C

The viscosities and specific heat capacities of twelve vegetable oils were experimentally determined as a function of temperature (35 to 180° C) by means of a temperature controlled rheometer and differential scanning calorimeter (DSC). Viscosities of the oil samples decreased exponentially with temperature. Out of the three models (modified WLF, power law, and Arrhenius) that were used to describe the effect of temperature on viscosity, the modified WLF model gave the best fit. The specific heat capacity of the oil samples however increased linearly with increase in temperature. The equations developed in the study could be valuable for designing or evaluating handling and processing systems and equipment that are involved in the storage, handling and utilization of vegetable oils.

Moisture Effect on the Physical Characteristics of Switchgrass Pellets

Switchgrass was pelleted through a 4.76 mm (3/16 in.) diameter die. The physical characteristics of the pellets were measured. It was found that the bulk density, particle density, durability, and hardness of the pellets were significantly affected by moisture content. The maximum values of bulk density and particle densities were 708 and 1462 kg/m3, respectively. The force required to rupture the pellets varied from 32 N at 6.3% moisture content to 22 N at 17.4% moisture content. Durability of the pellets was also affected by moisture content and was maximum at 8.6% (wet basis) moisture content. The pellets absorbed moisture at rates that were significantly affected by air relative humidity (P < 0.05). The EMC-ERH data for the pellets were sigmoidal in shape and were best predicted by the modified Chung-Pfost equilibrium moisture equation.

Thermal and Dielectric Properties of Sweetpotato Puree

Abstract Pureeing of sweetpotato (SP) is carried out to enhance the conversion of the roots into value‐added products. During processing, production and home utilization, the puree is often heated (conventional cooking or microwaved), hence the need to measure the corresponding properties of SP puree. Thermal (specific heat, thermal conductivity, density, and thermal diffusivity) and dielectric properties (dielectric constant and dielectric loss factor) of SP puree were determined within a temperature range of 5 to 80°C. Increase in temperature increased the specific heat (3.70–3.78 kJ/kg K), thermal conductivity (0.52–0.78 W/m K), and thermal diffusivity (1.98 × 10−7–4.25 × 10−7 m2/s) of SP puree. The density (705–485 kg/m3) of the puree decreased with temperature. Both temperature and frequency (900–2500 MHz) significantly affected the dielectric constant (60.5–73.0) and dielectric loss factor (16.5–29.5) of SP puree. At the two frequencies (915 and 2450 MHz) used in industrial food processing, calculations showed that the penetration depth was not significantly affected by temperature at 2450 MHz, while at 915 MHz, the penetration depth decreased with temperature.

Prediction of mixed hardwood lignin and carbohydrate content using ATR-FTIR and FT-NIR

This study used Attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy and Fourier transform near-infrared (FT-NIR) spectroscopy with principal component regression (PCR) and partial least squares regression (PLS) to build hardwood prediction models. Wet chemistry analysis coupled with high performance liquid chromatography (HPLC) was employed to obtain the chemical composition of these samples. Spectra loadings were studied to identify key wavenumber in the prediction of chemical composition. NIR-PLS and FTIR-PLS performed the best for extractives, lignin and xylose, whose residual predictive deviation (RPD) values were all over 3 and indicates the potential for either instrument to provide superior prediction models with NIR performing slightly better. During testing, it was found that more accurate determination of holocellulose content was possible when HPLC was used. Independent chemometric models, for FT-NIR and ATR-FTIR, identified similar functional groups responsible for the prediction of chemical composition and suggested that coupling the two techniques could strengthen interpretation and prediction.

Compaction of poultry litter.

Poultry litter, a combination of accumulated chicken manure, feathers and bedding materials, is a potential feedstock for bioenergy and other value-added applications. The use of this waste product has been historically limited to within few miles of the place of generation because of its inherent low density. Compaction is one possible way to enhance the storage and transportation of the litter. This study therefore investigates the effect of moisture content (19.8-70.7%, d.b.) and pressure (0.8-8.4 MPa) on the compaction characteristics of poultry litter. Results obtained showed that the initial density of densified poultry litter, energy required for compaction and the strength of the densified material after 2 months of storage were significantly (P<0.05) affected by moisture content and pressure applied during compaction. The density of the compacted material was only affected by pressure applied during compaction after 2 months of storage. The specific energy required to produce the densified material varied from 0.25 to 2.00 kJ/kg and was significantly less than the energy required to produce pellets from biological materials. The results obtained from the study can be used for the economical design of on-farm compaction equipment for poultry litter.

Rapid assessment of coniferous biomass lignin–carbohydrates with near-infrared spectroscopy

The main objective of this research was to construct accurate near-infrared reflectance (NIR) models of wood chemistry. Wet chemistry procedures and high-performance liquid chromatography methods were employed to analyze the chemical composition of southern pine. The NIR spectra were collected from 21 wood samples, which were milled down to different particle size classes. NIR calibration and prediction models were established using two modeling methods with different pretreatments. Furthermore, the spectrum range used in the NIR models was refined to achieve higher prediction accuracy. Results showed that NIR model precision could be improved considerably by decreasing the particle size to a very fine powder coupled with a targeted spectrum range. Superior prediction models for lignin and holocellulose content were constructed, while models for extractives and cellulose contents were also strong.

Non-destructive prediction of the properties of forest biomass for chemical and bioenergy applications using near infrared spectroscopy

Forest biomass will play a key role as a feedstock for bioproducts as the bioeconomy develops. Rapid assessment of this heterogeneous resource will help determine its suitability as feedstock for specific applications, aid in feedstock improvement programmes and enable better process control that will optimise the biorefinery process. In this study, near infrared spectroscopy coupled with partial least-squares regression was used to predict important chemical and thermal reactivity properties of biomass made up of needles, twigs, branches, bark and wood of Pinus taeda (loblolly pine). Models developed with the raw spectra for property prediction used between three and eight factors to yield R2 values ranging from a low of 0.34 for higher heat values to a high of 0.92 for volatile matter. Pretreating the raw spectra with first derivatives improved the fit statistics for all properties (i.e. min 0.57, max 0.92; with two or three factors). The best-performing models were for extractives, lignin, glucose, cellulose, volatile matter and fixed carbon (R2 ≥ 0.80, residual predictive deviation/ratio of performance to deviation ≥1.5). This study provided the capacity to predict multiple chemical and thermal/energy traits from a single spectrum across an array of materials that differ considerably in chemistry type and distribution. Models developed should be able to rapidly predict the studied properties of similar biomass types. This will be useful in rapidly allocating feedstocks that optimise biomass conversion technologies.

Identifying Plant Part Composition of Forest Logging Residue Using Infrared Spectral Data and Linear Discriminant Analysis

As new markets, technologies and economies evolve in the low carbon bioeconomy, forest logging residue, a largely untapped renewable resource will play a vital role. The feedstock can however be variable depending on plant species and plant part component. This heterogeneity can influence the physical, chemical and thermochemical properties of the material, and thus the final yield and quality of products. Although it is challenging to control compositional variability of a batch of feedstock, it is feasible to monitor this heterogeneity and make the necessary changes in process parameters. Such a system will be a first step towards optimization, quality assurance and cost-effectiveness of processes in the emerging biofuel/chemical industry. The objective of this study was therefore to qualitatively classify forest logging residue made up of different plant parts using both near infrared spectroscopy (NIRS) and Fourier transform infrared spectroscopy (FTIRS) together with linear discriminant analysis (LDA). Forest logging residue harvested from several Pinus taeda (loblolly pine) plantations in Alabama, USA, were classified into three plant part components: clean wood, wood and bark and slash (i.e., limbs and foliage). Five-fold cross-validated linear discriminant functions had classification accuracies of over 96% for both NIRS and FTIRS based models. An extra factor/principal component (PC) was however needed to achieve this in FTIRS modeling. Analysis of factor loadings of both NIR and FTIR spectra showed that, the statistically different amount of cellulose in the three plant part components of logging residue contributed to their initial separation. This study demonstrated that NIR or FTIR spectroscopy coupled with PCA and LDA has the potential to be used as a high throughput tool in classifying the plant part makeup of a batch of forest logging residue feedstock. Thus, NIR/FTIR could be employed as a tool to rapidly probe/monitor the variability of forest biomass so that the appropriate online adjustments to parameters can be made in time to ensure process optimization and product quality.

Thermophysical Properties of SweetPotato Puree at Freezing and Refrigeration Temperatures

The thermophysical properties (initial freezing point, unfreezable water, enthalpy of freezing, and specific heat) of alginate-restructured sweet potato (SP) puree at freezing and refrigeration temperatures were determined using differential scanning calorimetry. Restructuring of SP puree increased the amount of unfreezable (bound) water in the puree from 0.44 g H2O/g solids to about 0.56 g H2O/g solids and reduced the freezing point from −2.5 to −3.2°C. During freezing (or melting), the specific heat increased from about 1.9 to 90 kJ/kg. After freezing, the specific heats of restructured and nonrestructured SP puree were respectively 3.695 and 3.404 kJ/kg. Between 358 and 403 kJ/kg of heat have to be removed when SP puree (at 20°C) is to be frozen to −40°C.