Hydrothermal Conversion of Lignocellulosic Biomass to Hydrochar: Production, Characterization, and Applications

The high moisture content poses a major technical barrier to using wet biomasses in thermochemical conversions. Hydrothermal conversions open efficient ways to convert wet biomass into carbonaceous products as an alternative to thermochemical methods such as pyrolysis, gasification, and combustion. Three types of hydrothermal conversions, hydrothermal carbonization (HTC), hydrothermal liquefaction (HTL), and hydrothermal gasification (HTG), use different operating conditions to convert wet biomass into distinct products: solid (hydrochar), liquid (aqueous soluble bio-oil), and gaseous fractions. Water plays a dominant role in hydrothermal conversions. HTC uses relatively mild conditions. HTL and HTG use subcritical and supercritical conditions, respectively. Conversion mechanisms and the effect of process parameters are also discussed in detail. The solid product hydrochar (HC) has properties comparable to biochar and activated carbon, hence a range of potential applications. Current and emerging applications of HC, including energy production and storage, soil amendment, wastewater treatment, carbon capture, adsorbent, and catalyst support, are discussed

Recent Advances in Thermochemical Conversion of Biomass

The chapter focuses on recent trends of biomass conversion into valuable energy, chemicals, gaseous and liquid fuels. Biomass is presently the largest source of renewable energy and the primary bioenergy resource in the world. A comprehensive discussion on different types, sources and compositions of biomass is presented. The most abundant biomass on the earth is lignocellulose and it represents a major carbon source for chemical compounds and biofuels. The chapter presents a thorough review of lignocellulosic biomass and the importance of biomass as a renewable source. It then reviews biomass classification and composition. It introduces the analysis of biomass feedstock. Biomass is converted to energy, chemicals and clean fuels using various conversion techniques such as thermochemical, chemical and biochemical. The chapter provides a thorough examination of thermochemical conversion processes that use high temperatures to break down the bonds of organic matter. It briefly introduces combustion and gasification, followed by a comprehensive review of different pyrolysis techniques

Lactic Acid Production from Lignocellulosic Biomass

This chapter presents bio-based lactic acid production process from lignocellulosic biomass. Bio-based chemicals can replace the chemicals that we usually get from petroleum-based resources, and they are used to produce cleaners, solvents, adhesives, paints, plastics, textiles, and many other products. Lactic acid is one of such candidates of bio-based chemicals with important applications in various industrial sectors such as the chemical, pharmaceutical, food, and cosmetics industries, where its demand is steadily increasing. It is also an essential building block for numerous commodity and intermediate-biobased chemicals making it as a suitable alternative to their fossil-derived counterparts. The bioconversion process of transforming lignocellulosic biomass into lactic acid consists of four primary stages. Initially, pretreatment is performed to enable the utilization of all C5 and C6 sugars by the selected microorganism. These sugars are then hydrolyzed and fermented by a suitable microorganism to produce either L- or D-lactic acid, depending on the desired stereochemistry. Finally, the lactic acid is separated and purified from the fermentation broth to obtain a purified product. The promising method for the industrial production of bio-based lactic acid will be of continuous simultaneous saccharification and fermentation in a gypsum-free process using Mg(OH)2 as neutralizer, followed by reactive distillation for purified lactic acid production. The cradle-to-gate life cycle assessment model for the biobased lactic acid production process indicated that the about 80–99% of the environmental burdens of most of the environmental impact categories can be reduced compared with its equivalent fossil-based lactic acid, making biobased lactic acid environmentally superior to the fossil-based lactic acid

Recent Perspectives in Biochar Production, Characterization and Applications

This chapter presents the most promising features and applications of biochar along with their optimal pyrolysis conditions. Biochars have a range of physicochemical properties depending on the feedstock and pyrolysis conditions, which greatly affect their wide applications. The biochar production and its characteristics, including the effect of feedstocks and different process-parameters on the properties and yield of biochar are thoroughly examined. The higher pyrolysis-temperature can give higher carbon-contents, pH, and surface-areas of biochars while volatiles and molar-ratios of O/C, H/C and N/C decrease with pyrolysis-temperature. Higher carbon-content and neutral-pH biochars have high affinity for organic pollutants due to high surface areas, making them attractive for adsorption and catalysis purposes. Biochars with higher-pH are preferred for soil application to correct soil-acidity. Thus, the pyrolysis temperature should be selected as per the final application of the biochar. Characterization of biochars of different feedstocks and pyrolysis conditions is reviewed and presented along with their proximate and ultimate analysis

Prediction of lamb tenderness using combined quality parameters and meat surface characteristics

The objectives of the present study were: to investigate the predictability of cooked lamb tenderness from textural parameters extracted from lamb chops images using GLRM and GLDM techniques. To study the combined effects of texture features, marbling and ultimate pH on the prediction models

Vision based methodology for evaluation of meat quality characteristics : A thesis submitted in partial fulfilment of the requirements for the Degree of Doctor of Philosophy at Lincoln University

Meat quality is a subject of growing interest. Important meat quality parameters include colour, tenderness, texture, flavour and odour, water holding capacity and juiciness. The quantification of meat quality is a challenge of major importance in the meat industry. The ability to meet consumer expectations by providing quality products and maintaining the consistency of products is the basis for the long-term success of suppliers in the competitive meat industry. Visual assessment has become a main component of meat quality evaluation. Visual assessment is subjective, inconsistent and highly variable. It is not capable of detecting commercially important attributes of carcasses. Objective measurements are needed to ensure consistency and reproducibility of meat quality evaluation. Vision based methods have been shown to be an effective means for meat quality evaluation. At present there is no online method available in New Zealand to measure meat quality characteristics, but there is a real need to determine these characteristics as the market moves to supply meat to the quality specifications required by modem consumers. This is reflected by large supermarkets internationally dictating quality specifications to processors with the aim of capturing consumers and market share. The objective of this project was to investigate the possibilities of computer vision based approaches in lamb quality evaluation and to provide an imaging system, which can be used at processor and retail operations, as an interactive system. The information available in the captured images is translated into parameters, which relate to meat quality characteristics. This imaging system should be able to determine the grade, the relative abundance of different components of meat (lean meat, marbling, etc) and, more importantly, some eating quality characteristics. The individual modules involved in a computer vision system are image acquisition, pre-processing, segmentation, image analysis, feature extraction, feature pre-processing, feature selection and classification. Illumination is a very important aspect in image acquisition. The specular reflections caused by moisture on the meat surface are a major problem in meat imaging. The polariser-analyser filters were used to eliminate most of these reflections. The next important steps in a vision system are image pre-processing, segmentation and analysis. Image processing is used to pre-process images before computer analysis. The first step in image pre-processing was background removal. Edge enhancement, filtering and/or sharpening techniques were then used to remove any noise present and to enhance the visual quality of the images. Proper segmentation algorithms are very important for a successful vision system. Colour, brightness and edge detection were used to segment images into regions. Image analysis quantified the elements of interest like lean, fat, connective tissue, marbling and bone areas from the meat images. Feature extraction is an important pre-processing method for classification; perhaps it is the heart of a pattern recognition system. Geometric and texture information are important elements of a computer vision system. Twelve geometric (area and thickness) features were measured from lamb chop images. In addition, 136 texture features were also measured. These include 36 grey-level difference histogram (first-order texture) features, 90 grey-level co-occurrence matrix (second-order texture) features and 10 grey-level run length matrix (higher-order texture) features. Feature selection is another important aspect of pattern recognition. It simplifies data by eliminating redundancy and isolating the important characteristics of the data, allowing better insight and concise representation of the data structure. Principal component analysis used for dimensionality reduction. During the dimensionality reduction, six geometric, eight co-occurrence, four run length and four grey-level difference histogram features were selected from an original set of features with total variance of 96.1%, 99.2%, 97.9% and 86.7%, respectively. Principal component scores were calculated as linear combinations of measured geometric variables. The other alternative to the principal component score is to select one of the measured variables to represent the principal component. In classification, we used both multivariate statistical and neural network analyses. The classification was performed in stages using reduced and original feature sets as well as principal component scores. Different combinations of these feature sets were used. In most cases, a reduced feature set produced better classification than principal component scores. The highest classification of 85.6% was achieved with original features (12 geometric + 10 GLRM + 36 GLDM), which was only 1 % higher than that of reduced features (6 geometric + 8 GLCM + 4 GLRM). It indicates that the reduced features have a similar discriminatory power as the original features. In all cases, the classification rates achieved with reduced features were still comparable, because of the simplicity in feature extraction. Analysis of misclassified images was performed to study the behaviour of the misclassifications. The classification using a single reduced feature set produced a few unusual misclassifications. Such misclassifications were minimised by combining the reduced feature sets. The lamb carcasses were regraded more objectively using fat thickness measurement (fat 11) and carcass weight, revealing that 41.3% of carcasses have previously been graded incorrectly mainly because of error in estimating fat thickness, and also some carcasses were graded incorrectly as higher weight carcasses. The highest classification of 88.8% was achieved using original features (12 geometric + 10 GLRM + 36 GLDM), which was only 2% higher than that of reduced features (6 geometric + 8 GLCM + 4 GLDM). Principal component scores produced a lower classification than the reduced features. The hot carcass weight was included in the classification using reduced and original feature sets and increased the accuracy of the overall classification in all cases. The highest classification of 89.4% was achieved with original features (12 geometric + 36 GLDM) and carcass weight, which was only 1.3% higher than the classification achieved with reduced features (6 geometric + 4 GLDM) and carcass weight. The data reduction was useful even if we include carcass weight as a variable in the classification. The neural network approach was attempted to further improve the results and search for possible non-linear relationships. Multi-layer perceptron networks containing back propagation learning algorithms were trained to classify lamb chop images into different grades. Several different neural networks and learning parameters were tested to select the best neural network. The highest classification rate of 96.9% was achieved with reduced features (6 geometric + 8 GLCM + 4 GLRM). The single reduced feature set produced a few unusual misclassifications and combining the reduced feature sets eliminated the unusual misclassifications. Single hidden layer networks with a number of hidden neurons were trained to classify lamb chop images into new grades. Several different learning parameters were tested to select the best neural network. The highest classification rate of 96.3% was achieved with reduced features (6 geometric + 8 GLCM + 4 GLRM + 4 GLDM). The neural network analysis was also performed with hot carcass weight added as an input variable in order to classify lamb chop images into old grades as well as new grades. The highest classification rate of 98.8% in predicting old grades was achieved using reduced features (6 geometric + 4 GLRM) in combination with carcass weight. The highest classification rate of 98.8% in predicting new grades was achieved using reduced features (6 geometric and 8 GLCM) in combination with carcass weight. The effect of number of hidden neurons and activation functions were also studied. The relationship between fat measurements using chemical analysis and image analysis was examined using best-fit models of the SPSS curve estimation procedure. Statistically, the relationship was best described by a non-linear regression. The equation obtained for the prediction of crude fat percentage from image analysis measurements was In(C) = e¹•²⁷⁵⁵⁻⁽⁸•⁶²⁹¹/ⁱ ⁾ (R² = 0.81). Multi-layer perceptron networks containing back-propagation learning algorithms were trained for the prediction of lamb tenderness. Several different neural networks and learning parameters were tested to select the best neural network. The effects of network parameters, architecture, different features and feature selection on prediction have been studied. The highest coefficient of determination of 0.746 in predicting lamb tenderness was achieved using the reduced (6 geometric + 8 GLCM) features. The primary objective of this research, investigating the possibilities of computer vision based approaches in lamb quality evaluation, was carried out successfully. The result of this work is an imaging system with image and texture analyses based feature extraction, multivariate statistical techniques based feature selection and a neural network based classification and prediction model

Advances in Bioenergy Production Using Fast Pyrolysis and Hydrothermal Processing

This chapter provides an overview of current efforts and advances as well as environmental and economic aspects of fast pyrolysis and hydrothermal processing, which are potential technologies for bioenergy production, mainly bio-oil and syngas. Biomass is presently the primary bioenergy resource in the world. The chapter presents a brief discussion of sources and compositions of biomass. Biomass is converted to various products using thermochemical conversions. Pyrolysis is a thermochemical process that converts biomass into carbon-rich solid residue, condensable vapors, and non-condensable gases in the absence of oxygen. It is a promising technology for converting biomass into renewable biofuels with environmental and economic advantages. Pyrolysis processes are classified based on their operating conditions and desired products. Two thermochemical processes, fast pyrolysis and hydrothermal processing are reviewed. Fast pyrolysis produces a higher quantity and quality of bio-oil and syngas than slow and intermediate pyrolysis processes. Hydrothermal processing converts wet biomass into carbonaceous biofuel. The ability to produce higher-value bioenergy by these pyrolysis technologies depends on the feedstock and operating condition of the pyrolysis processes. This chapter will present the most promising features of fast pyrolysis and hydrothermal processing along with their optimal pyrolysis conditions in maximizing the production of biofuels

Three neural network case studies in biology and natural resource management

This paper presents 3 NN case studies. In the first, fracture toughness of wood was predicted using an expanded MLP network from experimentally measured crack angle, stiffness, density and moisture content. The data is characterized by noise but the model produced physically meaningful nonlinear trends with an R2 value of 0.67. In the second study, hydraulic conductivity (K m/day) was estimated from ground water solute concentration data collected for a range of K values. Four separate NN needed to be developed for four sub-ranges of K to reduce error. In order to determine the appropriate range of K for a particular system concentration data were clustered into 4 groups using SOM. The hybrid-model was applied to an experimental aquifer and only 10% difference was found between experimental and NN estimations of K. In the third study, digital images of lamb chops were used to collect values for 102 geometric and textural variables for meat grading. Principal component analysis reduced the variables to twelve. Three- and four-layer MLP networks and discriminant function analysis (DFA) were performed on the data and the classification accuracy from 3-layer MLP was 83% and was 12% better than that from DFA

Lamb carcass classification system based on computer vision. Part 1, Texture features and discriminant analysis

This paper presents a lamb carcass classification system based on image and texture analyses together with multivariate statistical techniques (principal component analysis, cluster analysis and discriminant function analysis). Texture analysis is based on grey level co-occurrence matrix. A set of ninety texture features has been used to extract the texture information from the acquired images. In addition, twelve image area and thickness (geometric) variables have also been calculated. Principal component analysis was used to reduce the dimensionality of the original data set. Two feature sets were generated based on the results. These feature sets comprised of principal component (PC) scores calculated from the original variables and 14 (6 geometric and 8 texture) variables selected from the original set of variables. Both feature spaces were used for discriminant analysis. From the experimental results, it was established that the system enabled 66.3% and 76.9% overall classification based on 6 geometric PC scores and 14 (geometric and texture) PC scores, respectively. The system also enabled 64% and 79 % overall classification of lamb carcasses based on 6 geometric and 14 (geometric and texture) variables, respectively. This study shows the predictive potential of combining image analysis with texture analysis for lamb grading. The addition of carcass weight improved the overall classification accuracy, of both feature sets, to 85%

Lamb carcass classification system based on computer vision. Part 2, Texture features and neural networks

In this study, the ability of neural network models for lamb carcass classification was compared with a multivariate statistical technique with respect to the classification accuracy. The lamb carcass classification system is based on image and texture analyses. Digital images of lamb chops were used to calculate twelve image geometric variables. In addition, a set of ninety textural features was used to extract the textural information from the acquired images. Texture analysis is based on the grey level co-occurrence matrix method. Principal component analysis (PCA) was used to reduce the dimensionality of feature spaces. Two feature sets were generated. These feature sets comprised of 14 principal component (PC) scores calculated from the original variables and 14 variables selected from the original set of variables. Both feature spaces were used for neural network and discriminant analysis. Several network configurations were tested and the classification accuracy of 93% was achieved from three-layer multilayer perceptron (MLP) network. Its performance was 14% better than that from the Discriminant function analysis (DFA). The study shows the predictive potential of combining neural networks with texture analysis for lamb grading