Simultaneous Process Optimization Research Articles

Simultaneous process optimization and heat integration for ethylene-to-ethylene oxide process: A surrogate model-based approach

Since the production process of ethylene to ethylene oxide (EO) is highly energy-intensive, it is necessary to perform a whole process heat integration. However, due to the technical barriers, it is always a challenge to design the heat integration system while optimizing the process parameters by mechanistic models. In response to this issue, this study presented a novel surrogate model-based framework that allows for the seamless integration of process optimization and heat exchanger network (HEN) design. The EO production process is modeled and simulated to provide the essential data for surrogate model training. Considering the inherent complexity of the process and the challenges associated with model training, the whole production process is decomposed into parts and the output parameters relevant to heat integration are included within the surrogate models training by Artificial Neural Network. Building upon these preliminary steps, a flexible optimization framework built upon the Genetic Algorithm, integrating the developed process surrogate model and any HEN synthesis model, is constructed to achieve synchronous optimization. The results show that the obtained process saves 22.93 % utility cost and finally leads to a 2.60 % increase in total profit than the alternative designed by the conventional stepwise method, showing the priority of the proposed method.

Solute-induced grain refinement and defect suppression in boron-modified molybdenum manufactured via laser powder-bed fusion

Molybdenum manufactured with laser powder bed fusion (LPBF) has an undesirable coarse-grained, columnar microstructure interspersed with intergranular cracks, high porosity, and poor mechanical strength. These defects result from a combination of the harsh LPBF process conditions and the disadvantageous properties of molybdenum, such as its high brittle-ductile transition temperature and low tolerance for oxygen impurities. In order to suppress these defect-forming mechanisms and improve the suitability for LPBF, alloy-side material adjustments with simultaneous process optimization are necessary. In this work, the effect of adjusting Mo by adding 3.5 at.% B is investigated experimentally.Mo-3.5 at.% B specimens can be produced entirely free of cracks, with a density of 99.8%. The specimens have a microstructure of fine, equiaxed grains with an average grain size of 31 μm and an aspect ratio of 1.3, thus achieving substantial refinement of the otherwise typically coarse-grained columnar, anisotropic microstructure of pure Mo in LPBF. Furthermore, the grains possess a honeycomb-like cellular subgrain structure. This structure is formed through the solute rejection effect of B during the solidification and consists of initially solidified pure α-Mo cells with a cell size <1 μm and a honeycomb-like network of an ∼100 nm thick intercellular Mo2B phase completely covering the α-Mo cells. In addition, the formation of boron oxide inclusions, presumably B2O3, with a size of <50 nm within the Mo2B phase, provides an effective mechanism for scavenging oxygen impurities, thus ensuring segregation-free grain boundaries in Mo-3.5 at.% B.The microstructural modifications substantially improve the mechanical properties. Under appropriate process conditions, with the substrate plate preheating temperature playing a crucial role, a bending strength of 1120 ± 172 MPa and a hardness of 379 ± 24 HV10 at room temperature can be achieved. At a test temperature of 600 °C, an increase in the bending strength to 2265 MPa is observed, and the bending angle simultaneously increases from 2° at room temperature to 35° at 600 °C. These findings indicate that the strength of Mo-3.5 at.% B is limited by the brittle behavior of the material at lower temperatures, at which residual defects are likely to initiate fracture.

A Workflow for Crystallization Process Design with Simultaneous Process Optimization and Solvent Selection based on the Perturbed‐Chain Statistical Associating Fluid Theory

AbstractSolvent selection is a critical part of crystallization process design and is inherently intertwined with optimization of the operating conditions. Computer‐aided tools can greatly assist in solving these two problems simultaneously. However, the integration of predictive thermodynamic models and process optimization tools is often complicated, which may hamper industry adoption. This work presents a workflow for simultaneous solvent selection and process optimization for solution crystallization processes based on the perturbed‐chain statistical associating fluid theory (PC‐SAFT) equation of state. The workflow is provided with readily executable computational tools and aims to strike a balance between the resources needed to obtain experimental input data and good prediction performance. The use of the workflow is demonstrated through a case study involving aspirin crystallization, which shows that the workflow can provide suitable solvents and operating conditions for the crystallization process based on either cooling, antisolvent, or evaporative crystallization.

Data-driven simultaneous process optimization and adsorbent selection for vacuum pressure swing adsorption

Technologies for post-combustion carbon capture are essential for the reduction of greenhouse gas emissions to the atmosphere. However, they are still associated with high costs and energy consumption. Intensified processes for carbon capture have the potential to overcome these challenges due to their higher efficiency, lower capital cost, and increased operational flexibility. This work investigates simultaneous optimization of process conditions and adsorbent selection for a modular Vacuum Pressure-Swing Adsorption system designed for CO2 capture. Both surrogate-based Nonlinear Programming and Mixed-Integer Nonlinear Programming approaches are applied and compared in terms of computational efficiency and solution accuracy. Moreover, process performance results are examined by applying several data analytics techniques to gain insights into the material-process correlations. Data-driven classifiers and neural networks can accurately predict whether a material is likely to satisfy purity, recovery, and energy constraints when operated at optimal process conditions.

Integration of a cryogenic ASU within an IGCC process with simultaneous optimization and energy targeting

The effect of oxygen purity on the thermal efficiency of an enriched-oxygen-fed integrated gasification carbon cycle (IGCC) was studied. A simultaneous process optimization with heat integration framework is proposed and used for the study. Rigorous models were used to represent the cryogenic air separation unit (ASU), gasifier and combined cycle. It was found that the thermal efficiency was highest (41.3–42.3%) within a range of 35–85 mol% O2, with the optimum at 45.5 mol% O2. Furthermore, injection of the nitrogen-rich vapor distillate from the high-pressure column of the ASU to the gas turbine was found to be the optimal nitrogen integration scheme compared to no nitrogen integration or using vapor distillate from the low-pressure column. In the optimized solution, heat integration results showed that pre-heating of the gasifier's oxygen feed occurs at the pinch and steam turbine inlet pressure values were 165, 28.6 and 4.9 bar.

Simultaneous Optimization of an IGCC Process with Equation-Oriented Methods

Simultaneous optimization of process flowsheets using equation-oriented (EO) methods is a promising alternative to simulation studies. With the use of EO methods, simultaneous optimization of all process units including heat integration can be performed to achieve goals such as maximizing profit or minimizing operating costs. The use of EO methods required the development of new process models and equations of state (EOS), as algorithms and some formulations used in sequential methods do not work in an EO environment. Advancements in EO methods, along with the growth of gradient-based solvers, have enabled large-scale simultaneous flowsheet optimization. However, simultaneous process optimization still faces challenges as large problems, especially non-linear ones, may not achieve convergence without good initial values. A study on the simultaneous optimization of the integrated gasification and combined cycle (IGCC) process using EO methods was conducted as it is a combination of a cryogenic air separation unit (ASU), a coal gasifier and power generation units consisting of a combustor, a gas turbine, a heat recovery steam generator (HRSG) and a steam turbine. The purity of the oxygen product fed to the gasifier was often considered to be fixed in prior IGCC studies. With the ASU model and gasifier model used, the influence of oxygen purity and flowrate fed to the gasifier can be studied. The thermal efficiency of the IGCC process was maximized in GAMS using full-order EO process models. After the optimization, the thermal efficiency increased from 27.7 % in the base case to 36.7 %, with a notable change in process parameters, showing that the solver did not terminate close to the starting solution. Results showed that lowering the oxygen product purity from 95 % to 58.6 % increased the thermal efficiency of the IGCC process as the power required by the compressor in the ASU was reduced.

Simultaneous process optimization of ultrasound-assisted extraction of polyphenols and ellagic acid from pomegranate (Punica granatum L.) flowers and its biological activities

This study was designed to optimize the extraction rate of total polyphenols and ellagic acid from pomegranate flowers. Single factors were investigated for liquid-to-material ratio (5–25), ethanol concentration (20%–60%), sonication time (5–60 min), and sonication power (150–500 W). The level range of the Box-Bokhen design was determined with respect to the single-factor results. The components of each index were normalized using the entropy weighting method for obtaining the comprehensive evaluation value. Under the actual conditions, the final optimization results were 17 for liquid-to-material ratio, 43% for ethanol concentration, 10 min for ultrasonic time, and 300 W for ultrasonic power. The extracts obtained under optimal conditions were tested for the inhibition of Streptococcus mutans and its biofilm, and results showed that pomegranate flowers exerted some inhibitory effects on the bacterium. Phosphomolybdenum and FRAP assays were used, and DPPH, ABTS, and O2− radical scavenging tests were conducted, indicating that pomegranate flower extracts have good antioxidant capacity.

Simultaneous Process Optimization of Ultrasound-Assisted Extraction of Polyphenols and Ellagic Acid from Pomegranate ( &lt;i&gt;Punica Granatum L.&lt;/i&gt;) Flowers and Its Biological Activities

This study was designed to optimize the extraction rate of total polyphenols and ellagic acid from pomegranate flowers. Single factors were investigated for liquid-to-material ratio (5–25), ethanol concentration (20%–60%), sonication time (5–60 min), and sonication power (150–500 W). The level range of the Box-Bokhen design was determined with respect to the single-factor results. The components of each index were normalized using the entropy weighting method for obtaining the comprehensive evaluation value. Under the actual conditions, the final optimization results were 17 for liquid-to-material ratio, 43% for ethanol concentration, 10 min for ultrasonic time, and 300 W for ultrasonic power. The extracts obtained under optimal conditions were tested for the inhibition of Streptococcus mutans and its biofilm, and results showed that pomegranate flowers exerted some inhibitory effects on the bacterium. Phosphomolybdenum and FRAP assays were used, and DPPH, ABTS, and ∙O2- radical scavenging tests were conducted, indicating that pomegranate flower extracts have good antioxidant capacity.

Simultaneous process optimization and heat integration based on rigorous process simulations

This paper introduces a simultaneous process optimization and heat integration approach, which can be used directly with the rigorous models in process simulators. In this approach, the overall process is optimized utilizing external derivative-free optimizers, which interact directly with the process simulation. The heat integration subproblem is formulated as an LP model and solved simultaneously during optimization of the flowsheet to update the minimum utility and heat exchanger area targets. A piecewise linear approximation for the composite curve is applied to obtain more accurate heat integration results. This paper describes the application of this simultaneous approach for three cases: a recycle process, a separation process and a power plant with carbon capture. Case study results indicate that this simultaneous approach is relatively easy to implement and achieves higher profit and lower operating cost and, in the case of the power plant example, higher net efficiency than the sequential approach.

Computer-aided molecular design in the continuous-molecular targeting framework using group-contribution PC-SAFT

Computer-aided molecular design allows generating novel fluids fulfilling a set of target properties. An integrated design of fluid and process directly employs a process-based objective function. In this work, we solve the integrated process and fluid design problem using the continuous-molecular targeting computer-aided molecular design (CoMT–CAMD) framework. CoMT–CAMD exploits the molecular picture underlying the PC-SAFT equation of state. In the simultaneous optimization of process and fluid, relaxed pure component parameters allow for an efficient optimization. The result is a hypothetical optimal target fluid. In previous work, fluids showing similar performance as the target fluid were obtained from a mapping onto a database. Here, we integrate computer-aided molecular design to realize the actual design of novel fluids. The resulting method for fluid design is based on a group-contribution method for the PC-SAFT parameters (GPC-SAFT) and applied to the design of working fluids for Organic Rankine cycles and solvents for CO2 capture.

Systems and control challenges in photovoltaic manufacturing processes: A modeling strategy for passivation and antireflection films

A view of contemporary systems and control challenges in photovoltaic cell manufacturing is given in this paper, with emphasis on developing a modeling strategy for the optimization of silicon nitride SiNx:H films used for passivation and antireflection coatings in single and multicrystalline silicon solar cells. The overall framework integrates three modeling modules: a remote plasma-enhanced chemical vapor deposition reactor process model that predicts film composition and thickness based on process input parameters, a solar-optical module that relates antireflection film physical and chemical properties to the degree to which the spectral irradiance distribution is attenuated, and a solar cell device model that predicts cell power output and efficiency from the film properties and irradiance. Because the model couples process inputs to both photovoltaic cell performance and manufacturing process efficiency, the modeling approach can be used for the simultaneous optimization of process and product performance.

A rapid and simple protocol for evaluating biohydrogen production potential (BHP) of wastewater with simultaneous process optimization

Biohydrogen production utilizing negative valued waste through dark-fermentation process is one of the emerging areas. Reported conditions for H2 production are significantly variable and comparative analysis of data is major problem for unified understanding. A simple, rapid and generalized two phase methodology/protocol was developed to evaluate the biohydrogen production potential (BHP) of negative valued wastewater as substrate/feed-stock for renewable biohydrogen production using mixed consortia. Critical factors that can influence the overall process viz., redox condition, organic load and biocatalyst were considered in the designing the methodology. Feasibility of protocol was initially evaluated with synthetic wastewater and further validated with real field composite food and slaughter house wastewaters. The selected operational factors showed marked influence on both H2 production and wastewater treatment. The reported methodology/protocol not only provides the ability of selected wastewater to generate H2 but also facilitates process understanding based on selected factors and finally acquiesce optimum conditions.

Integration of process and solvent design towards a novel generation of CO2 absorption capture systems

A method for the integrated process and solvent design of absorption separation systems is proposed in this work. The method is employed here to improve the energetic performance of a pre-combustion CO2 absorption capture process by simultaneous optimization of process and solvent variables. In the proposed design method, the discrete nature of solvent molecules is relaxed avoiding the introduction of integer variables in the solution of the optimization problem. For this purpose, a molecular-based thermodynamic model, the perturbed-chain polar (PCPSAFT) equation of state, is used to establish a direct relationship between the process performance and the molecular characteristics of a hypothetical target solvent. Real solvent candidates are selected in a subsequent stage of molecular mapping by assessing substances from a data-base, using a Taylor approximation of the objective function. The simultaneous design method is shown to yield major improvements of the performance of CO2 absorption processes.