The present study summarizes the results of macro- and micro-mixing characteristics in an airlift internal loop reactor with low aspect ratio (H/D≤5) using the electrolytic tracer response technique and the method of parallel competing reactions respectively. The micro-mixing has never been investigated in airlift loop reactors. The dual-tip electrical conductivity probe technique is used for measurement of local bubble behavior in the reactor. The effects of several operating parameters and geometric variables are investigated. It is found that the increase in superficial gas velocity corresponds to the increase in energy input, liquid circulation velocity and shear rate, decreasing the macro-mixing time and segregation index. Moreover, it is shown that top clearance and draft diameter affect flow resistance. However, the bubble redistribution with a screen mesh on the perforated plate distributor for macro-mixing is insignificant. The top region with a high energy dissipation rate is a suitable location for feeding reactants. The analysis of present experimental data provides a valuable insight into the interaction between gas and liquid phases for mixing and improves the understanding of intrinsic roles of hydrodynamics upon the reactor design and operating parameter selection.

Multivessel batch distillation (MVBD) is mainly used to separate mixtures with more than two components. In this article, a new operation mode with MVBD is proposed for separation of binary mixtures under total reflux. A mathematic model is setup for the simulation. The proposed operation policy and the regular operation with constant reflux are compared theoretically and experimentally. The results show that the new operation mode has great advantages in time saving and operation flexibility. MVBD presents great potential for separation with high efficiency.

This research proposes a modified two-dimensional Peng-Robinson equation model to predict adsorption isotherm in adsorbate-adsorbent systems. The parameters of the proposed model are calculated by using the optimization of experimental data for the different single gas adsorption systems at various temperatures. The experimental adsorption equilibrium data of adsorbate-adsorbent systems was compared with the calculated results in our proposed model and the two-dimensional Hill-deBoer equation model. The proposed model as indicated in the results shows a better prediction of the experimental results compared with two others.

The paper describes a method for monitoring CO₂ leakage in geological carbon dioxide sequestration. A real time monitoring parameter, apparent leakage flux (ALF), is presented to monitor abnormal CO₂ leakage, which can be calculated by atmospheric CO₂ and O₂ data. The computation shows that all ALF values are close to zero-line without the leakage. With a step change or linear perturbation of concentration to the initial CO₂ concentration data with no leakage, ALF will deviate from background line. Perturbation tests prove that ALF method is sensitive to linear perturbation but insensitive to step change of concentration. An improved method is proposed based on real time analysis of surplus CO₂ concentration in least square regression process, called apparent leakage flux from surplus analysis (ALFs), which is sensitive to both step perturbation and linear perturbations of concentration. ALF is capable of detecting concentration increase when the leakage occurs while ALFs is useful in all periods of leakage. Both ALF and ALFs are potential approaches to monitor CO₂ leakage in geosequestration project.

This paper combines grey model with time series model and then dynamic model for rapid and in-depth fault prediction in chemical processes. Two combination methods are proposed. In one method, historical data is introduced into the grey time series model to predict future trend of measurement values in chemical process. These predicted measurements are then used in the dynamic model to retrieve the change of fault parameters by model based diagnosis algorithm. In another method, historical data is introduced directly into the dynamic model to retrieve historical fault parameters by model based diagnosis algorithm. These parameters are then predicted by the grey time series model. The two methods are applied to a gravity tank example. The case study demonstrates that the first method is more accurate for fault prediction.

The present investigation targets minimum cost of reactors in series for the case of one single chemical reaction, considering plug flow and stirred tank reactor(s) in the sequence of flow reactors. Using Guthrie's cost correlations three typical cases were considered based on the profile of the reaction rate reciprocal versus conversion. Significant differences were found compared to the classical approach targeting minimum total reactor volume.

In this paper, an improved nonlinear process fault detection method is proposed based on modified kernel partial least squares (KPLS). By integrating the statistical local approach (SLA) into the KPLS framework, two new statistics are established to monitor changes in the underlying model. The new modeling strategy can avoid the Gaussian distribution assumption of KPLS. Besides, advantage of the proposed method is that the kernel latent variables can be obtained directly through the eigen value decomposition instead of the iterative calculation, which can improve the computing speed. The new method is applied to fault detection in the simulation benchmark of the Tennessee Eastman process. The simulation results show superiority on detection sensitivity and accuracy in comparison to KPLS monitoring.

In low-temperature processes, there are interactions between heat exchanger network (HEN) and refrigeration system. The modification on HEN of the chilling train for increasing energy recovery does not always coordinate with the minimum shaft work consumption of the corresponding refrigeration system. In this paper, a systematic approach for optimizing low-temperature system is presented through mathematical method and exergy analysis. The possibility of "pockets", which appears as right nose section in the grand composite curve (EGCC) of the process, is first optimized. The EGCC with the pockets cutting down is designed as a separate part. A case study is used to illustrate the application of the approach for a HEN of a chilling train with propylene and ethylene refrigerant system in an ethylene production process.

This paper presents a life cycle assessment (LCA) based biofuel supply chain (SC) analysis framework which enables the study of economic, energy and environmental (3E) performances by using multi-objective optimization. The economic objective is measured by the total annual profit, the energy objective is measured by the average fossil energy (FE) inputs per MJ biofuel and the environmental objective is measured by greenhouse gas (GHG) emissions per MJ biofuel. A multi-objective linear fractional programming (MOLFP) model with multi-conversion pathways is formulated based on the framework and is solved by using the ε-constraint method. The MOLFP problem is turned into a mixed integer linear programming (MILP) problem by setting up the total annual profit as the optimization objective and the average FE inputs per MJ biofuel and GHG emissions per MJ biofuel as constraints. In the case study, this model is used to design an experimental biofuel supply chain in China. A set of the weekly Pareto optimal solutions is obtained. Each non-inferior solution indicates the optimal locations and the amount of biomass produced, locations and capacities of conversion factories, locations and amount of biofuel being supplied in final markets and the flow of mass through the supply chain network (SCN). As the model reveals trade-offs among 3E criteria, we think the framework can be a good support tool of decision for the design of biofuel SC.

In the past two decades, short-term scheduling of multipurpose batch plants has received significant attention. Most scheduling problems are modeled using either state-task-network or resource-task-network (RTN) process representation. In this paper, an improved mixed integer linear programming model for short-term scheduling of multipurpose batch plants under maximization of profit is proposed based on RTN representation and unit-specific events. To solve the model, a hybrid algorithm based on line-up competition algorithm and linear programming is presented. The proposed model and hybrid algorithm are applied to two benchmark examples in literature. The simulation results show that the proposed model and hybrid algorithm are effective for short-term scheduling of multipurpose batch plants.

Industrial ecological system is a sustainable mode of modern industry development. Industrial symbiosis, a sub-field of industrial ecology, engages traditionally separate industries in a collective approach, involving exchange of materials, energy, water, and/or by-products, to enhance competitive ability and environmental performance. To construct a symbiosis analysis method, this article employs a number of parameters embodying information about materials, energy and economics as the main essential parameters in system analysis and introduces symbiosis profit and symbiotic consumption elements as the economic indicators. A modeling and simulation program is designed using the agent-based modeling approach to simulate the evolvement of a hypothetical coal-based industrial system and the change of symbiosis conditions in the process of construction is examined. The simulation program built using the Swarm library, which is a freely available multi-agent simulation package, provides a useful demonstration for the symbiosis analysis method.

Experiments were carried out to investigate the influences of cation from electrolytes and acidity/alkalinity on the phase behavior of sodium dodecyl sulfate-n-butanol-organics-water (with electrolytes) microemulsion system. The organics used is commercial kerosene. The volume ratio of water to organics is 1:1. The results show that the type and valence of electrolyte cations are important factors influencing the microemulsion behavior. Bivalent Ca²⁺ is more effective than monovalent K⁺ and Na⁺ for the formation of Winsor type III and II microemulsion. For electrolytes with the same monovalent cation Na⁺, #em/em#.e. NaCl and Na₂CO₃, anions in the electrolyte have some effect. Bivalent anion leads to a lower activity of cation Na⁺ than monovalent anion Cl^-. NaOH (or KOH) behaves similar with NaCl (or KCl). When HCl is used as electrolyte, its acidity plays an important role. Phase inversion of microemulsion from type III (or II) to type I is observed through precipitation of Ca²⁺ using Na₂CO₃, neutralization of HCl by NaOH, and addition of water to the system, which releases the oil from the microemulsion.