Controllability reflects the ease that a process can be controlled in practical operating environment. However, an unclear influence between the HEN synthesis and the control structure selection has been not investigated for the work of controllability of heat exchanger network (HEN). To address this challenge, this paper proposes a multi-objective optimization method by considering both the quantitative measures of economic and controllability, i.e., minimizing the total annual cost (TAC) and the relative gain array number (RGAn). This method is developed using a HEN synthesis procedure where a model-based superstructure is employed to involve the set of the network configuration alternatives and all the potential control structures. The effects of minimum approach temperature (ΔT_min) on the multi-objective optimization problem are investigated to distinguish the consistent and opposite variations of TAC and RGAn. The consistent change enables us to solve the single objective optimization problem for economical HEN design as well as for taking controllability into account. The opposite change prompts the Pareto front of the two objectives in order to develop a trade-off strategy. Results indicate that this method helps in the determination of the relationship-based nature between network configuration and control structure to yield a HEN design with economic and controllability considerations.

Bioethanol can be efficiently produced from lignocellulosic biomass via two-phase processes, consisting of enzymatic hydrolysis and fermentation. To enhance economic and energy efficiency, a system combining separate hydrolysis and fermentation of biomass with a direct-ethanol solid oxide fuel cell (SOFC) is proposed in this work. The system comprises six units: a pretreatment reactor unit, a conditioning unit, a high-solids hydrolysis unit, a seed train unit, an ethanol recovery unit, and an SOFC unit. Exergy analysis based on a thermodynamic model indicates a total exergy efficiency of approximately 0.72. Within the high-solids hydrolysis unit, one piece of equipment exhibits the lowest exergy efficiency of 0.21, at a biomass flux of 71,510 kg/h. The other main exergy destruction exists in the conditioning unit and is followed by seed train unit, accounting for 5.61 and 2.77 of total exergy destruction ratios, respectively. In addition, the tentative parametric analysis for reaction kinetics is performed with varying reaction orders. The results indicate that ammonia gas in a specific unit can follow first- or second-reaction order, whereas acetic acid and sulfuric acid exhibit zero-reaction order, due to the gradual conversion of cellulose to glucose. This work provides key insights for the practical design and operation of the proposed separate hydrolysis and fermentation–SOFC system.

Microalgae have attracted great research interest as a feedstock for producing a wide range of end-products. However, recent studies show that the tight processing integration technology for microalgae-based biorefinery makes production less economical and even has a negative impact on sustainability. In this study, a new two-tier superstructure optimization design methodology is proposed to locate the optimal processing pathway. This model is developed based on the decomposition strategy and the relationship-based investigation, coupling an outer-tier structure with an inner-tier structure, wherein the outlet flows of the middle stages is relaxed and then an appropriate level of redundancy for designing the processing is provided. Two scenarios are developed to compare the most promising biorefinery configurations under two different design option favors. By solving the mixed integer nonlinear programming model with the objective functions of maximizing the yield of the desired products and maximizing the gross operating margin, the optimization results obtained show the ability of this framework to provide the promising configurations and cost-effectiveness of microalgae-based biorefinery. Compared with Scenario 1, the optimized solutions in Scenario 2 feature a gross operating margin increase up to 27.09% and an increase in product yield up to 25.00%. The proposed method improves the original huge computing scale and ensures economics without simplifying the processing pathways.

Improving mass transfer in gas diffusion layers is critical to achieving high-performance proton-exchange membrane fuel cells (PEMFCs). Leaks through the interface between the gas and the membrane electrode assembly frame have been widely investigated, and the controllability of the cathode gas diffusion has not been achieved in most studies. In this study, we develop a structural parameter to investigate the controllability of the gas diffusion mechanism in the cathode in order to improve upon the design and performance of PEMFCs. This parameter accounts for the cathode gas diffusion layer porosity and carbon loading inside the catalyst layer. It is comprehensively calculated to relax the two segments’ distribution along three directions of the coordinate axis. The experimental and simulation results show that the obtained values of the parameter vary and change during voltage stabilization. According to the results, regardless of the materials in the cathode gas diffusion layer, the same steady-state voltage is obtained when the parameter is fixed. The cell could be controllably operated for a wide range of diffusion layer thicknesses by selecting the optimal parameter.

Corn stover is an abundant, low-cost, and representative lignocellulosic agricultural residue for bioethanol production, but its recalcitrant structure requires effective pretreatment to improve downstream conversion performance. Ball milling technology has been widely used to pretreat lignocellulosic biomass, where regulating particle size distribution enhances lignin utilization and subsequent ethanol production. However, systematic optimization of ball milling operating parameters remains insufficiently investigated. To bridge the gap between bench-scale experimentation and process-level performance prediction, this study establishes an integrated experimental and simulation framework. Ball milling experiments are conducted to systematically vary key operating parameters, and particle morphology is precisely quantified through optical imaging coupled with digital image analysis. Empirical correlations between these operating conditions and particle size characteristics are derived through polynomial fitting. These correlations are then embedded into an Aspen Plus process model for bioconversion performance evaluation. Evaluation from this integrated framework reveals that system-level optimization of the pretreatment process effectively reduces mean particle size, narrows size distribution, and increases specific surface area. By systematically evaluating the operating space, an optimal window that yields a 2.97% improvement in glucose conversion is identified. This work provides practical guidance and a generalizable framework for optimizing mechanical pretreatment to maximize target product yields.