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Steam cracking of naphtha is an important process for the production of olefins. Applying artificial intelligence helps achieve high-frequency real-time optimization strategy and process control. This work employs an artificial neural network (ANN) model with two sub-networks to simulate the naphtha steam cracking process. In the first feedstock composition ANN, the detailed feedstock compositions are determined from the limited naphtha bulk properties. In the second reactor ANN, the cracking product yields are predicted from the feedstock compositions and operating conditions. The combination of these two sub-networks has the ability to accurately and rapidly predict the product yields directly from naphtha bulk properties. Two different feedstock composition ANN strategies are proposed and compared. The results show that with the special design of dividing the output layer into five groups of PIONA, the prediction accuracy of product yields is significantly improved. The mean absolute error of 11 cracking products is 0.53wt% for 472 test sets. The comparison results show that this indirect feedstock composition ANN has lower product prediction errors, not just the reduction of the total error of the feedstock composition. The critical factor is ensuring that PIONA contents are equal to the actual values. The use of an indirect feedstock composition strategy is a means that can effectively improve the prediction accuracy of the whole ANN model.

By introducing low-entanglement UHMWPE, the mechanical properties of polyolefins are improved to varying degrees. For polypropylene, the lack of interaction between UHMWPE and polypropylene results in an unsatisfactory reinforcement effect, and the disentangled state makes it easier for the particles to form defects driven by a chain explosion. In contrast, regarding polyethylene and elastomer containing ethylene segments, low-entanglement UHMWPE plays a better role in reinforcement. A series of measurements including scanning electron microscopy (SEM), rheological measurements, differential scanning calorimetry (DSC), and mechanical measurement were used to investigate the mechanisms for the different enhancement effects. It originates from interdiffusion and entanglement forming of polyethylene segments across the interface, endowing the material with different aggregated and defect structures. For instance, EPDM possesses a higher optimal dosage of UHMWPE particles reflected in good interfacial interdiffusion with UHMWPE particles, leading to significant optimized mechanical performance.

High density polyethylene/n-Hexane system was adopted to investigate the bubbles nucleation and collapse in polymer melt with a visualization apparatus at different temperatures. The results showed that the initial bubble number, initial total bubble volume and the mean bubble diameter all increased with the temperature increasing, and the bubble diameter distribution changed from single peak distribution to bimodal distribution. Moreover, the effect of temperature on the maximum bubble diameter was mainly reflected in the change of the pressure in bubbles due to the change of the volatile content in the polyethylene melt. For bubble collapse, the collapse time became longer but the bubble collapse rate became faster with the temperature increasing, and a fitting model was established in which the collapse coefficient n was used to describe the collapse rate. Through dimension analysis, the correlation between the collapse coefficient n and the melt surface tension σ, zero-shear melt viscosity η0, diffusion coefficient D, maximum bubble diameter dmax, melt density ρ, initial bubble number density Nini were established and the variation of D with temperature could be predicted as an exponential correlation.

In order to reduce the waste heat emission and environmental impact of industrial processes, Organic Rankine Cycle (ORC) is gradually used for energy recovery. ORC is regarded as the most promising measure for converting low-grade heat into electricity, but commercial applications are still limited due to the high investments and poor economic returns. However, the simultaneous optimization of ORC and Heat Integration can improve system economy. This work proposes a techno-economic optimization model involving the area estimate of heat exchanger based on vertical heat transfer for optimization of ORC and Heat Integration. This model determines the selection of working fluids, the optimal operating parameters of ORC including temperatures, pressures, and flowrate of working fluids. Both of the supercritical and subcritical conditions can be considered in this model. To solve this optimization problem, a bi-level optimization approach is developed, where the outer level uses Genetic Algorithm to identify the promising working fluids and optimize the temperatures and pressures of ORC, and the inner level is an NLP model to find the optimal flowrate of ORC and the vertical matches of streams by minimizing total annual cost. The results represent the necessity of simultaneous optimization of Organic Rankine Cycle and Heat Integration.

Molecular reconstruction is an effective tool to predict the detailed composition of complex mixtures under obtainable bulk properties and chemical details. With the introduction of statistical distribution, although the number of variables in the model is reduced, the ranges of statistical distribution parameters have a great influence on the prediction accuracy. A novel parameter estimation method using the analysis of the probability density plot of gamma distribution based on the basic available information is proposed to obtain parameter ranges with wide universality and non-specificity. The obtained ranges are used in a molecular reconstruction model to predict the bulk properties and molecular composition of naphtha. The model assumes molecular composition within each homologous series follows a gamma distribution against carbon number. The results of a naphtha sample show that the calculated bulk properties and molecular composition are in good agreement with the experimental values.

Hydrogen resource can be saved by reuse and purification reuse between refineries. Thus, it’s necessary to optimize the inter-plant hydrogen network in a petrochemical complex. In a petrochemical complex, purifiers are employed to recover hydrogen components from off gas. However, purifier models such as PSA, membrane units have non-linear characteristics. Even if minimizing hydrogen utility, mathematical programming models adopted by most of the researchers belong to the non-convex and non-linear models, not guaranteeing global optimal solution. In this paper, a novel mathematic programming model for the optimization of inter-plant hydrogen network with purification unit is presented. If only the hydrogen utility consumption is minimized, the model is linear, guaranteeing global optimal solution. If the object comes to economic benefits such as pipeline cost, the objective function is nonlinear. The hydrogen utilities in refinery can be reduced by the inter-plant optimization and the number of inter-plant connections can be optimized to minimum. Case study indicates that the model is effective.

There is a growing interest in developing Total Site Heat Integration methods, since Total Site Heat Integration offers an energy-saving opportunity across multiple plants beyond the traditional intra-plant plant integration. Total Site heat integration can be achieved indirectly by non-isothermal utility loop, such as hot water and thermal oil. The parameters of non-isothermal utility loop, interconnectivity patterns directly determine the energy saving and the capital cost. In this work, a transshipment type Total Site heat exchanger network (HEN) model using non-isothermal utility loop is developed. A new representation method of non-isothermal utility loop is proposed based on transshipment type HEN model to avoid non-linear terms. The model is formulated as a MINLP problem with all linear constraints. The operating cost, capital cost of heat exchangers, pumping and piping cost are holistically considered and optimized. The case study demonstrates the effectiveness of the proposed model.

The quantitative structure-property relationship (QSPR) of constrained geometry catalysts (CGCs) has been analyzed by combining density functional theory (DFT) and multivariate linear regression (MLR). QSPR models for determining the reaction energy barriers of ethylene and 1-octene and molecular descriptors were established and validated with a testing set. At the initiation stage, the quaternary QSPR models for ethylene and 1-octene feature R² values of 0.90 and 0.82, respectively, while the corresponding leave-one-out Q² values for ethylene and 1-octene are determined to be 0.82 and 0.69. Using the generated QSPR models, the electronic effect was proven to be more critical for determining the energy barriers of ethylene and 1-octene insertion processes. At the propagation stage, the quaternary QSPR models for ethylene and 1-octene feature R² values of 0.97 and 0.96, respectively, while the corresponding leave-one-out Q² values for ethylene and 1-octene are determined to be 0.93. Particularly, the impact of the steric effect on the energy barriers of ethylene and 1-octene insertion is more pronounced. Furthermore, the correlation between the corresponding energy barriers and experimental activities with three series of CGCs was discussed, which revealed that theoretical calculations are consistent with the experimental results. [ABSTRACT FROM AUTHOR]