Due to the presence of high content of oxygenated compounds (aldehydes, alcohols, carboxylic acids, esters, ethers, furfurals, ketones, lignin-derived compounds, phenols, and sugars), bio-oil has inferior oil properties compared to petroleum-derived oils. This creates numerous technological challenges in downstream separation processes. The present study outlines recent research trends on various separation strategies for upgrading crude biogenic pyrolysis oil for the production of valuable commodities. The present study mainly focuses on the various separation strategies, such as column chromatography, distillation, membrane filtration, crystallization, solvent extraction, electrosorption, and fractional condensation with respect to principles of operation, efficiency, economy and environmental concerns. Phase separation using solvent and adsorbent was found to be the best separation strategy compared to others due to lower capital investment and energy expenditure. However, there are various technological challenges with separation strategies for scale-up in industries. A comparative analysis of various separation strategies with the application of various bio-oil fractions from aqueous phases of bio-oil is summarized to understand the possible pathways for utilization in various industries. A brief section on technoeconomic analysis with existing pilot and semi-pilot pyrolysis plants is presented to understand the economic feasibility of pyrolysis and upgrading strategies. In the end, a circular economy perspective of pyrolysis-separation and its integration with a machine learning model, are briefly outlined.

The global concerns regarding climate change and the steep increase in greenhouse gas emissions, driving the society to transform biogenic waste into renewable value-added products. Thermal depolymerization of biomass is regarded as the most promising thermochemical conversion technology to produce bio-oil, biochar and syngas. To date, previously published review articles revolve around various biomass pyrolysis aspects, such as the chemistry of biomass, application of pyrolysis products, effects of pyrolysis parameters and kinetics, effect of various catalysts on product yield, upgradation strategies, and process technologies. The commercialization of conventional pyrolysis technology is challenging due to the inferior oil properties (oxygenates nearly 40 wt%), agglomeration, feeding constraints, ash content in the biomass, heat and mass transfer limitations, pressure build-up due to tar formation and thermal distribution across the reactor. Pressurized steam pyrolysis of biomass improves the product quality (oil, char and gas) with a production of value-added chemicals. Despite this, studies regarding the combined effect of steam and pressure on product quality from pyrolysis technology are not available in the literature. This study offers a comprehensive overview of the state-of-the-art on the effect of pressure and steam on biogenic pyrolysis. Additionally, the study also explains how pressure and steam can be utilized to improve the properties of the pyrolysis products. The review examine the fundamentals of biomass conversion, the effect of pressure and steam, with interlaid mechanisms on biomass conversion and challenges with pressurized steam pyrolysis. Finally, the benefits of products in various applications and a conceptual process perspective of pressurized steam pyrolysis are briefly outlined.

Thermal depolymerization of lignocellulosic biomass at 400-600 oC in the absence of oxygen yields liquid, char, and gas. The presence of high oxygenates (40 wt.%) in the liquid fraction contributes to deleterious properties such as low heating value, high viscosity, corrosiveness, and low thermal stability. The present study investigates an upgrading strategy for crude biogenic pyrolysis oil using various adsorbents (calcium hydroxide, red mud, bentonite, dolomite, used silica, and new silica) with petroleum ether as a diluent. The textural and microstructural features of the adsorbents are analysed using various characterization techniques - a scanning electron microscopy (SEM) and a Brunauer–Emmett–Teller (BET) specific surface area analyser. The objective of the study was to induce a separation of soluble and insoluble phases in petroleum ether (upper homogeneous fraction and lower non-homogeneous fraction with adsorbents) using various cheap adsorbents, and to find the best adsorbent for the refining process. The original crude oil and the refined oil from the upgradation strategy were extensively characterized using multiple analytical techniques to understand the chemical, thermal, and stability characteristics. The results showed that calcium hydroxide is more effective in the removal of oxygenates in comparison with other adsorbents due to variations in surface area.

The upgrading of crude biogenic pyrolysis oil (CO) was carried out using a simple scalable, inexpensive upgrading strategy based on the use of various adsorbents. The upgraded oil and the efficiency of the process were extensively characterized using various analytical techniques. The preliminary objective of the study was to identify the best adsorbent for refining CO. The adsorbents were characterized using scanning electron microscopy (SEM) and a Micromeritics 3Flex Instrument to determine the morphology and surface area of the adsorbents used. Crude and upgraded oil were extensively characterized to understand chemical composition, stability, and thermal properties. The importance of the study is to remove oxygenated compounds in CO using industrial waste adsorbents and solvent. The present upgrading strategy separates CO into an upper homogeneous soluble phase in petroleum ether and a lower non-homogeneous insoluble phase in petroleum ether. Oxygenates are reduced from 40.13 wt.% to 0.14 wt.% with the use of calcium hydroxide as an adsorbent and petroleum ether as a solvent. Finally, a discussion on the overview of the upgradation strategy is briefly summarized at the end of the manuscript

According to reports by Safe Food Advocacy Europe 2024, 220 million tons of plastic waste are disposed of as waste. Among them, 69.5 million tons end up in the environment, which poses hazards to humans and aquatic organisms. Pyrolysis is a thermochemical recycling method that converts plastics into liquid, solid, and gaseous products in an inert atmosphere at 400-600 oC. The objective of the study is to predict the product yield using various machine learning algorithms (linear regression, decision trees, random forest, gradient boosting machines and support vector machines). The challenges with plastic pyrolysis industries are the need for capital investments and operating expenditure, difficulties in pilot-scale experiments and optimization of product quality due to diversity in the composition of plastics. In comparison with the literature, the present work incorporates a variety of feedstocks such as lowdensity polyethylene (LDPE), polypropylene (PP), acrylonitrile butadiene styrene (ABS), high density polyethylene (HDPE), polycarbonate (PC) and polystyrene (PS) to predict the product yield and quality from plastic pyrolysis. The methodology comprises data collection from published literature, processing of numerical parameters (temperature and pressure), and categorical parameters (feedstock, catalyst, reactor type). The product outputs (solid, liquid, and gas) are correlated with published findings, ensuring that they fall within realistic ranges. Solid yields are typically observed between 5% and 15%, liquid yields range from 60% to 90%, and gas yields are calculated to ensure the total sums to 100%. Furthermore, the dataset is split into train and test sets in the ratio of 80:20 followed by handling outliers using isolation forest, data visualization, and evaluation of best models for the yield prediction. The preliminary data analysis showed that the random forest model showed a satisfactory R-square score of 0.92 compared to other models due to its ensemble behaviour.  Temperature was found to be the dominant parameter in influencing liquid and gas yield. The heat correlation map showed that carbon content has a strong positive correlation with solid yield and a negative correlation with gas yield (Fig. 1). The prediction can be improved by using a large volume of data sets, incorporating new machine learning algorithms and the testing with pilot scale data sets. The graphical user interface for plastic pyrolysis would be a better option for pyrolysis industries to optimize product quality and yield from a variety of feedstocks (Fig. 2). Figure 1: Heat map of Pearson correlation matrix  Figure 2: Graphical user interface for predicting pyrolysis yield from plastic pyrolysis.