Solvent-based carbon capture processes typically suffer from the temperature rise of the solvent due to the heat of absorption of CO2. This increased temperature is not thermodynamically favorable and results in a significant reduction in performance in the absorber column. As opposed to interstage coolers, which only remove, cool, and return the solvent at discrete locations in the column, internal coolers that are integrated with the packing can cool the process inline, which can result in improved efficiency. This work presents the modeling of these internal coolers within an existing generic, equation-oriented absorber column model that can cool the process while allowing for simultaneous mass transfer. Optimization of this model is also performed, which is capable of optimally choosing the best locations to place these devices, such that heat removal and mass transfer area are balanced. Results of the optimization have shown that optimally placed cooling elements result in a signi... [more]

Cyclic adsorption processes attain a cyclic-steady state (CSS) condition by undergoing repeated cycles in time, owing to their transient and modular nature. Mathematically, solving a set of underlying nonlinear partial differential equations iteratively for different steps in a cycle until the CSS condition is attained presents a computational challenge, making the simulation and optimization of cyclic adsorption processes time-consuming. This paper focuses on expediting the CSS convergence in adsorption process simulations by implementing two vector-based acceleration methods that offer quadratic convergence akin to Newton’s methods. These methods are straightforward to implement, requiring no prior knowledge of the first derivatives (or Jacobian). The study demonstrates the efficacy of accelerated convergence by considering two adsorption processes that exhibit complex dynamics, namely, a four-step vacuum swing adsorption and a six-step temperature swing adsorption cycles for post-co... [more]

Developing methods for rapid, large-scale deployment of carbon capture systems is critical for meeting climate change goals. Optimization-based decisions can be employed at the design and manufacturing phases to minimize the costs of deployment and operation. Manufacturing standardization results in significant cost savings due to economies of numbers. Building on previous work, we present a process family design approach to design a set of carbon capture systems while explicitly including economies of numbers savings within the formulation. Our formulation optimizes both the number and characteristics of the common components in the platform and simultaneously designs the resulting set of carbon capture systems. Savings from economies of numbers are explicitly included in the formulation to determine the number of components in the platform. We show and discuss the savings we gain from economies of numbers.

In this work, we present recent algorithmic and implementation advances of the nonconvex two-stage robust optimization solver PyROS. Our advances include extensions of the scope of PyROS to models with uncertain variable bounds, improvements to the formulations and/or initializations of the various subproblems used by the underlying cutting set algorithm, and extensions to the pre-implemented uncertainty set interfaces. The effectiveness of PyROS is demonstrated through the results of an original benchmarking study on a library of over 8,500 small-scale instances, with variations in the nonlinearities, degree-of-freedom partitioning, uncertainty sets, and polynomial decision rule approximations. To demonstrate the utility of PyROS for large-scale process models, we present the results of a carbon capture case study. Overall, our results highlight the effectiveness of PyROS for obtaining robust solutions to optimization problems with uncertain equality constraints.

The determination of the design space (DS) in a pharmaceutical process is a crucial aspect of the quality-by-design (QbD) initiative which promotes quality built into the desired product. This is achieved through a deep understanding of how the critical quality attributes (CQAs) and process parameters (CPPs) interact that have been demonstrated to provide quality assurance. For computational inexpensive models, the original process model can be directly deployed to identify the design space. One such crucial process is the Tablet Press (TP), which directly compresses the powder blend into individual units of the final product or adds dry or wet granulation to meet specific formulation needs. In this work, we identify the design space of input variables in a TP such that there is a (probabilistic) guarantee that the tablets meet the quality constraints under a set of operating conditions. A reduced-order model of TP is assigned for this purpose where the effects of lubricants and glidan... [more]

Superstructure optimization for process synthesis is a challenging endeavour typically leading to large scale MINLP formulations. By the combination of phenomena-based building blocks, accurate thermodynamics, and structural screening we obtain a new framework for optimal process synthesis, which overcomes prior limitations regarding solution by deterministic MINLP solvers in combination with accurate thermodynamics. This is facilitated by MOSAICmodeling’s generic formulation of models in MathML / XML and subsequent decomposition and code export to GAMS and C++. A branch & bound algorithm is implemented to solve the overall MINLP problem, wherein the structural screening penalizes instances, which are deemed nonsensical and should not be further pursued. The general capabilities of this approach are shown for the distillation-based separation of a ternary system.

We present new modifications to superstructure optimization paradigms to i) enable their robust solution and ii) extend their applicability. Superstructure optimization of chemical process flowsheets on the basis of rigorous and detailed models of the various unit operations, such as in the state operator network (SON) paradigm, is prone to non-convergence. A key challenge in this optimization-based approach is that when process units are deselected from a superstructure flowsheet, the constraints that represent the deselected process unit can be numerically singular (e.g., divide by zero, logarithm of zero and rank-deficient Jacobian). In this paper, we build upon the recently-proposed modified state operator network (MSON) that systematically eliminates singularities due to unit deselection and is equally applicable to the context of both simulation-based and equation-oriented optimization. A key drawback of the MSON is that it is only applicable to the design of isobaric flowsheets... [more]

Maintaining product integrity in multi-product oil pipelines is crucial for efficiency and profit. This study presents a strategy combining design and process improvement to enhance flushing protocols, addressing the challenge of residual batch contamination. A pilot plant, mirroring industrial operations through dimensionless residence time distribution, was developed to identify and rectify bottlenecks during product transition. The pilot plant’s success in replicating industrial operations paves the way for targeted experiments and modelling to enhance optimized flushing, ensuring product quality and operational excellence.

This paper presents an algorithm for developing sparse surrogate models that satisfy mass/energy conservation even when the training data are noisy and violate the conservation laws. In the first step, we employ the Bayesian Identification of Dynamic Sparse Algebraic Model (BIDSAM) algorithm proposed in our previous work to obtain a set of hierarchically ranked sparse models which approximate system behaviors with linear combinations of a set of well-defined basis functions. Although the model building algorithm was shown to be robust to noisy data, conservation laws may not be satisfied by the surrogate models. In this work we propose an algorithm that augments a data reconciliation step with the BIDSAM model for satisfaction of conservation laws. This method relies only on known boundary conditions and hence is generic for any chemical system. Two case studies are considered-one focused on mass conservation and another on energy conservation. Results show that models with minimum bia... [more]

In designing low-carbon processes, the unintended emission of CO2 remains a significant concern due to its global environmental impact. This paper explores carbon management within chemical processes, specifically examining the correlation between the process material balance (PMB) and CO2 emissions to understand and identify the potential for reducing these emissions. We interrogate the foundational issue of carbon discharge by analyzing the interplay among mass, energy, and entropy balances, which collectively influence the PMB. We introduce the concept of the Target Material Balance (TMB), which represents the material balance of a process corresponding to minimum CO2 emissions within the given constraints. We could ask what decisions in the design and operation of processes result in higher CO2 emissions than the TMB. We will focus on the interaction between reactions and recycles and how the arrangement of recycles in processes can inadvertently change the PMB, thereby increasing... [more]

Sustainability encompasses many wicked problems involving complex interdependencies across social, natural, and engineered systems. We argue holistic multiscale modeling and decision-support frameworks are needed to address multifaceted interdisciplinary aspects of these wicked problems. This review highlights three emerging research areas for artificial intelligence (AI) and machine learning (ML) in molecular-to-systems engineering for sustainability: (1) molecular discovery and materials design, (2) automation and self-driving laboratories, (3) process and systems-of-systems optimization. Recent advances in AI and ML are highlighted in four contemporary application areas in chemical engineering design: (1) equitable energy systems, (2) decarbonizing the power sector, (3) circular economies for critical materials, and (4) next-generation heating and cooling. These examples illustrate how AI and ML enable more sophisticated interdisciplinary multiscale models, faster optimization algor... [more]

Alginate, a promising biopolymer in the food, biomedical, pharmaceutical, and electronic materials industries, is characterized by its biodegradability, biocompatibility, low toxicity, and gel-forming properties. It is most abundantly found in brown algae. However, conventional dilute acid and alkali extraction methods face limitations in commercialization due to their long processing time, low throughput, and high solvent requirements. In this study, a microwave-assisted extraction (MAE) process for sodium alginate was designed to improve extraction efficiency. The solid/liquid ratio, extraction temperature, and extraction solvent concentration were major variables affecting sodium alginate extraction from Undaria pinnatifida (sea mustard). They were then statistically optimized using response surface methodology. Under optimal conditions (13.27 g/L, 91.86 °C, 2.51% (w/v), and 15 min), the yield was 38.41%, which was 93.43% of the theoretical content of sodium alginate in Undaria pinn... [more]

This paper presents an approach for optimizing the oil recovery factor by determining initial oil production rates. The proposed method utilizes the Q-learning method and the reservoir simulator (Eclipse 100) to achieve the desired objective. The system identifies the most efficient initial oil production rates by conducting a sufficient number of iterations for various initial oil production rates. To validate the effectiveness of the proposed approach, a case study is conducted using a numerical reservoir model (SPE9) with simplified configurations of two producer wells and one injection well. The simulation results highlight the capabilities of the Q-learning method in assisting reservoir engineers by enhancing the recommended initial rates.

Optimizing the multilayer thermal insulation of pipelines transporting liquids and gases at higher than ambient temperatures is crucial for heat energy conservation and cost optimization. This study utilizes a multi-objective genetic algorithm to optimize the multilayer thermal insulation thickness around a pipe carrying fluid to minimize heat loss and associated costs. The model adopted mathematical associations between design variables and the overall installation cost of layers over a pipe from the available literature. The proposed model considered one or more insulation layers of rock wool and calcium silicate to oil pipelines containing steam, furfural, reduced crude or 300-distillate oil. All calculations considered fixed-charge rates as a fraction of 1 or 0.15. The results were compared with standard values and those predicted by other researchers in the literature. For the steam line, the standard insulation thickness was 50 mm, jumping to 327 mm for rock wool and 232 mm for c... [more]

In this paper, various thermal energy systems are studied to recover waste heat from gas turbines with different configurations. The exergy analysis and environmental examination are applied to achieve better insight into the suggested systems. Also, multi-objective optimization is employed to find the optimal condition of the introduced plants. In this work, various systems such as gas turbine (GT), organic Rankine cycle (ORC), and Kalina cycle (KC) with Proton Exchange Membrane (PEM) electrolyzer are combined to achieve a new system design. In this study, Engineering Equation Solver (V11.755) and Matlab (R2023a) software are used to simulate and optimize the proposed system. The comparison of systems shows that the combustion chamber with 3622 kW has the most considerable exergy destruction in the IGT/ORC-KC plant. The comparative investigation shows that IGT/ORC-KC has the highest output at 5659 kW, while the smallest exergy destruction is associated with the IGT system with 1779 kW... [more]

The ball and balancer system is a popular research platform for studying underactuated mechanical systems and developing control algorithms. It is a well-known two-dimensional balancing problem that has been addressed by a variety of controllers. This research work proposes two controllers that are proportional integral derivative-second derivative-proportional integrator (PIDD2-PI) controller and tilt integral derivative with filter (TID-F) controller in a multivariate, electromechanical, and nonlinear under-actuated ball and balancer system. Integral Time Absolute Error (ITAE) is an objective function used for designing controllers because of its ability to be more sensitive to overshooting as well as reduced settling time and steady-state error. As part of the analysis, four metaheuristic optimization algorithms are compared in the optimization of proposed control strategies for cascaded control of the ball and balancer system. The algorithms are the Grey Wolf optimization algorithm... [more]

Given that ultrasound-assisted aqueous extraction is gaining importance within “green technology” and to increase the efficiency of extracting bioactive compounds from Levisticum officinale root waste, optimization of its parameters was undertaken. Multi-objective (multi-criteria) optimization can be an extremely promising tool not only for designing and analyzing the extraction process, but also for making process-control decisions. Therefore, the main objective of this study was to develop and optimize an environmentally friendly ultrasound-assisted extraction methodology for the aqueous extraction of bioactive compounds from the roots of Levisticum officinale, which are considered a by-product. The focus was on determining the optimal extraction conditions of the independent variables, such as solid−liquid ratio, extraction time and ultrasound power, so that the optimized extracts present the highest bioactive potential expressed in terms of levels of phenolic compounds, flavonoids,... [more]

Sugarcane bagasse (SB) is a widely available agro-industrial waste residue in China that has the potential to be converted into a cost-effective and renewable adsorbent. In this study, activated carbon (AC) was prepared from SB by microwave vacuum pyrolysis using H3PO4 as the activator. To enhance the sorption selectivity and yield, the pyrolysis process of SB-activated carbon (SBAC) should be well-designed. Central composite design was employed as an optimized experiment design, and response surface methodology was used to optimize the process parameters for maximized SBAC yield and its iodine number. The results showed that the optimized parameters obtained for the SBAC are 2.47 for the impregnation ratio (IR), 479.07 W for microwave power (MP), 23.86 mm for biomass bed depth, and 12.96 min for irradiation time, with responses of 868.7 mg/g iodine number and 43.88% yield. The anticipated outcomes were substantiated, revealing a marginal 5.4% variance in yield and a mere 1.9% discrepa... [more]

Hard materials have found extensive applications in the fields of electronics, optics, and semiconductors. Parallel grinding is a common method for fabricating high-quality surfaces on hard materials with high efficiency. However, the surface generation mechanism has not been fully understood, resulting in a lack of an optimization approach for parallel grinding. In this study, the surface profile formation processes were analyzed under different grinding conditions. Then, a novel method was proposed to improve surface finish in parallel grinding, and grinding experiments were carried out to validate the proposed approach. It was found that the denominator (b) of the simplest form of the rotational speed ratio of the grinding wheel to the workpiece has a great influence on surface generation. The surface finish can be optimized without sacrificing the machining efficiency by slightly adjusting the rotational speeds of the wheel or the workpiece to make the value of b close to the ratio... [more]

In addressing the issue of a low drawing rate in a steeply inclined and extremely thick coal seam, this study focused on the engineering background of the +575 horizontal working faces in the Wudong Coal Mine. By utilizing physical similarity simulation experiments, research was carried out on the top-coal-drawing rate and the gangue ratio at different coal-drawing intervals in horizontal segment mining for steeply inclined and thick coal seams, in which the relationships between the top-coal-drawing law and the drawing interval and technologies were revealed. The discrete element method was used to establish a numerical simulation model for the horizontal segment mining of steeply inclined and thick coal seams, and the roof-drawing law in the cases of the three-interval-group-of-support and drawing-once-every-two-support methods were analyzed before finally obtaining the optimal drawing technology. Through field practice, the coal-drawing effect of the technology was verified. The res... [more]

In order to improve the erosion resistance of steel PDC (Polycrystalline Diamond Compact) bit under high-speed fluid flow conditions underground, it is necessary to develop a high-performance erosion-resistant coating. In this paper, laser cladding was used to prepare the new coating by modifying tungsten carbide with graphene. And the effects of tungsten carbide content and graphene content on the coating performance have been thoroughly studied and analyzed to obtain the optimal covering layer. The research results indicate that, for new coatings, 60% tungsten carbide and 0.3% graphene are the optimal ratios. After adding tungsten carbide, the hardness has significantly improved. However, when the tungsten carbide content further increases more than 30%, the increase in hardness is limited. In addition, when the content of graphene is more than 0.3%, the branched structure becomes thicker. In detail, this is a phenomenon where the segregation of Cr, Si, and W becomes very obvious aga... [more]

The selection of growth substrate geometries for the mechanical dry transfer of carbon nanotubes to device substrates depends on the precision of the assembly equipment. Since these geometries play a decisive role in the overall efficiency of the process, an investigation of the most important geometry parameters is carried out. The substrate geometry affects the number of carbon nanotubes suspended during the growth process and the speed of mechanical assembly at the same time. Since those two criteria are interlinked and affect productivity, a meta-model for the growth and selection of the nanotubes is simulated and a time study of the resulting assembly motions is subsequently performed. The geometry parameters are then evaluated based on the total number of suspended carbon nanotubes and the throughput rate, measured in transfers per hour. The accuracy specifications are then taken into account. Depending on the overall accuracy that can be achieved, different offset angles and ove... [more]

The separation section of the methanol-to-olefin (MTO) process is energy-intensive, and the optimization and heat integration can enhance energy efficiency and reduce costs. A bi-level optimization model framework is proposed to optimize the separation process and simultaneously integrate the heat exchanger network (HEN). The upper level employs a data-driven BP neural network proxy model instead of the mechanism model for the separation process, while the lower level adopts a stage-wise superstructure for the HEN without stream splits. The interaction between the two systems is realized effectively through information exchange. A bi-level particle swarm algorithm is employed to optimize complex problems and determine the optimal operational parameters for the distillation system and HEN. Compared with the typical sequential synthesis method, the optimization by the proposed approach reduces the total annual cost by 1.4293×106 USD/y, accounting for 4.76%.

Micro-pits are widely used in the aerospace and tribology sectors on cylindrical surfaces and electrochemical micromachining which are of great significance for the high material removal rate, absence of tool wear, and mechanical stress, while facing significant challenges such as stray corrosion and low machining efficiency. Aiming at the above problems, this paper proposes a comprehensive method called radial ultrasonic rolling electrochemical micromachining (RUREMM) in which an ultrasonic field has been added onto the cylindrical surface. First, a theoretical model was created to gain the rules of the formation and collapse of bubbles in the liquid medium. Second, to analyze the optimal size of the cathode electrode, the COMSOL5.2 simulation software was proposed to research the influence of the electric field on the different dimensions, and the influences of different parameters in RUREMM on material depth/diameter ratio and roughness are explored through processing experiments. R... [more]

To eliminate or reduce stick−slip vibration in torsional vibration of the drilling string and improve the rate of penetration (ROP), a stick−slip vibration model of the drilling string considering the ROP was established based on the multidimensional torsional vibration model of the drilling string. The model was verified by simulation analysis. The characteristics of the drilling string stick−slip vibration in the three stages of stationary, slip, and stick were analyzed. This paper investigated the influence of rotary torque, rotary speed, and weight on bit (WOB) on stick−slip vibrations in the drill string. Based on this, the relationship between the drilling parameters and ROP was established. Drilling parameter optimization was completed for soft, medium-hard, and hard formations. Results showed that appropriately increasing torque and decreasing WOB can reduce or even eliminate stick−slip vibrations in the drill string and increase the ROP. The parameter optimization increased th... [more]