The global reliance on fossil fuels has caused severe environmental challenges, emphasizing the urgent need for sustainable and renewable energy sources. Bioethanol production from lignocellulosic biomass has emerged as a promising alternative due to its abundance, renewability, and carbon-neutral footprint. However, its economic feasibility remains a major obstacle owing to high production costs, particularly those associated with low ethanol titers and the energy-intensive distillation process costs for low titers. High-solid loading processes (≥15% w/w or w/v) have demonstrated potential to overcome these limitations by minimizing water and solvent consumption, enhancing sugar concentrations, increasing ethanol titers, and lowering downstream processing cost. Nevertheless, high-solid loading also introduces operational bottlenecks, such as elevated viscosity, poor mixing, and limited mass and heat transfer, which hinder enzymatic hydrolysis efficiency. This review critically examines emerging pretreatment and enzymatic hydrolysis strategies tailored for high-solid loading conditions. It also explores techniques that improve sugar yields and conversion efficiency while addressing key technical barriers, including enzyme engineering, process integration, and optimization. By evaluating these challenges and potential mitigation strategies, this review provides actionable insights to intensify lignocellulosic ethanol production and advance the development of scalable, cost-effective biorefinery platforms.

A two-stage anaerobic digestion (AD) process has been applied to improve the efficiency of methane production from various organic materials. However, the performance of traditional process controllers may be limited by differences in the rate of biochemical reactions, process uncertainties, and the consequences of interconnection between the two bioreactors. In this work, a nonlinear optimization-based control strategy that applies an analytical model predictive control (AMPC) scheme with an adaptive optimal set-point is proposed for the control of the two-stage AD system. The objectives of the proposed control system are to stabilize the system under uncertain operating conditions and maximize biomethane production. The optimal set-points for the controller are adapted in real-time operation, and then the control system is performed to manipulate the controlled output to the optimal trajectories. Compensators and nonlinear state observers are applied to handle the process/model mismatch and estimate unmeasured variables. The proposed control system is applied to the process with disturbances, fluctuations of inlet stream concentrations, and changes in the bacterial growth rate, and the control performance is investigated. Simulation results show that the developed control scheme automatically adjusts the optimal set-points and provides adequate control actions to maintain the maximum rate of methane production. The results of this investigation demonstrate that the control strategy promotes different biochemical reactions, avoids the inhibition effect, and handles the mutual effects between acidogenic and methanogenic bioreactors for methane production effectively.

Oriental Journal of Chemistry is a peer reviewed quarterly research journal of pure and applied chemistry. It publishes standard research papers in almost all thrust areas of current chemistry of academic and commercial importance. It provides a platform for rapid publication of quality research papers, reviews and chemistry letters. Oriental Journal of Chemistry is abstracted and indexed in almost all reputed National and International agencies.