Circular economy is recognized as one of the most effective strategies for promoting plastic sustainability. However, its implementation requires to enhance consumer engagement, which remains a primary target of regulatory initiatives designed to promote plastic circular economy. To ensure sustained consumer participation, it is essential to evaluate and optimize various incentives, including regulatory policies, voluntary programs, and market-related mechanisms. This study applies Stackelberg Game Approach to quantitatively capture the strategic interactions between the authorities (as the leader) and consumers (as followers). The model incorporates key consumer behaviors, i.e., "use less," "use longer," and "recycling", to reflect their role in advancing plastic circular economy goals. By integrating factors such as governmental utility (gains of benefits), consumer utility (welfare), and plastic waste reduction, the model identifies the optimal intensities of various public initiati... [more]

Distillation is not only a widely-used but also an energy-intensive separation process, in which internals such as structured packings play an important role. Increasing mass transfer efficiency by designing improved structured packings in order to provide a large interfacial area while enabling low pressure drop is one promising approach to quickly reduce the energy requirements of vacuum distillation where low pressure drop is important for separation efficiency and thermal stability of the processed media. The current work presents an innovative method to optimize structured packings by means of constrained shape optimization on the basis of computational fluid dynamics simulations to minimize the pressure drop while maintaining a constant specific surface area. To solve the fluid dynamic optimization problem, a gradient-based local optimization algorithm in a continuous adjoint formulation is utilized. The shape optimization is applied for a commonly used Rombobak packing, and test... [more]

The evaluation of control properties in industrial processes is essential to achieve sustainability, a very relevant topic today. This study emphasizes the importance of control studies to ensure that processes are efficient, operable and safe. While strategies such as process intensification can reduce the size, cost, and consumption of energy, it can present challenges in control and operability. This work focuses on the evaluation of the control properties of schemes with different degrees of intensification for the purification of levulinic acid, with the aim of identifying designs with the best control properties and the best economic and environmental indicators. The schemes were designed under a systematic synthesis strategy and optimized using the hybrid method of differential evolution with a tabu list, considering the total annual cost and Eco-indicator 99. An open-loop study analyzed the relationship between manipulable variables and output variables using total condition nu... [more]

Maintaining operational efficiency while ensuring safety is a longstanding challenge in industrial process control, particularly in high-risk environments. This paper presents a novel Dynamic Risk-Informed Explicit Model Predictive Control (R-eMPC) framework that integrates safety and operational objectives using probabilistic constraints and real-time risk assessments. Unlike traditional approaches, this framework dynamically adjusts safety thresholds based on Bayesian updates, ensuring a balanced trade-off between reliability and efficiency. The validation of this approach is illustrated through a case study on tank level control, a safety-critical process where maintaining the liquid level within predefined safety limits is paramount. The results demonstrate the framework’s capability to optimize performance while maintaining robust safety margins. By emphasizing adaptability and computational efficiency, this research provides a scalable solution for integrating safety into real-ti... [more]

Enhanced weathering (EW) of alkaline minerals in chemical reactors with a controlled environment is recognized as a promising approach for gigaton-level carbon dioxide removal. However, reactor configuration and operating conditions must be optimized to balance the interfacial areas between gas, liquid and solid phases prior to industrial application. We developed a physics-based and data-driven hybrid modelling approach, coupled with multi-objective optimization, to study and compare three typical chemical reactors, i.e., trickle bed, packed bubbling columns, and stirred slurry reactors, and the optimal design to improve CO2 capture rate and reduce energy and water consumptions. Then an adaptive optimization is proposed to dynamically adjust the operating of the reactors in response to intermittent CO2 emission and renewable energy supply. Results indicated that forced stirring enhances CO2 capture rates by accelerating mass transport but increases energy consumption. Trickle bed reac... [more]

Seawater desalination is considered as the only available solution that can cope with the increasing demand for freshwater around the world. Improving the desalination techniques may help to cut off the cost and increase sustainability. In this paper, a mathematical model describing the MED process is developed within gPROMs software. The model includes all the necessary mass and energy balance equations together with thermodynamic and physical properties equations. The model predictions are validated against the actual plant data before using the model for optimizing the process to achieve minimum heat transfer area. For two different operating conditions (summer and winter) and a fixed production demand, the heat transfer area is minimised while optimising different parameters of the MED process. The results showed that a 10.4% reduction in the heat transfer area can be achieved under summer operating conditions and around 26% decrease in the heat transfer area can be met under winte... [more]

The Reverse Osmosis (RO) system has the potential as a vibrant technology to generate high-quality water from brackish water sources. Nevertheless, the progressive growth in water and electricity demands necessitates the development of a sustainable desalination technology. This can be achieved by reducing the specific energy consumption of the process, which would also reduce the environmental footprint. This study proposes the concept of reducing the overall energy consumption of a multistage multi-pass RO system of Arab Potash Company (APC) in Jordan via heating the feed brackish water. The utilisation of waste heat generated from different units of production plant of APC such as steam condensate supplied to a heat exchanger is a feasible technique to heat brackish water entering the RO system. To systematically assess the contribution of water temperature on the performance metrics including specific energy use, a generic model of RO system is developed. Model based simulation is... [more]

Process optimisation and quality control are crucial in process industries for minimising product waste and improving plant economics. Identifying robust operational regions that ensure both product quality and performance is particularly valued in industries. However, this task is complicated by operational uncertainties, which can lead to violations of product quality constraints and significant batch discards. We propose a novel robust optimisation strategy that integrates advanced machine learning and process systems engineering to systematically identify optimal operational regions under uncertainty. Our approach begins by using a process model to screen a broad operational space across various uncertainty scenarios, pinpointing promising control trajectories to satisfy process constraints and product quality. Machine learning is then employed to cluster these trajectories into sub-regions. Finally, a two-layer dynamic optimisation framework is employed to determine the optimal co... [more]

Asserting the phase stability of multi-component mixtures is an important task in computer-aided mixture/blend design (CAMbD), but it is often hindered by the lack of reliable and tractable models. In this paper, we propose a group-contribution machine-learning (GC-ML) method to predict phase coexistence for a large set of ternary mixtures consisting of two solvents and one (fixed) solute. Each solvent is represented by a vector of functional group numbers, encoded by integer values. The solvent vectors are combined with mixture composition and temperature to form the input features to a GC-ML surrogate classifier, which distinguishes between four types of stable phase configurations as possible outputs: liquid (L), solid-liquid (SL), liquid-liquid (LL) or solid-liquid-liquid (SLL). To explore the performance of the trained GC-ML multi-classifier, it is embedded as a surrogate phase-stability constraint in the optimisation of an ibuprofen crystallisation process. A two-step solution s... [more]

Recently, the focus on increasing process efficiency to reduce energy consumption has driven the adoption of alternative systems, such as Petlyuk distillation columns. It has been proven that, when compared to conventional distillation columns, these systems offer significant energy and cost savings. From an economic standpoint, achieving high-purity products alone does not ensure the feasibility of a process. Instead, balancing the trade-off between product purity and cost necessitates multi-objective optimization. While conventional optimization methods are effective, novel strategies like Bayesian optimization offer distinct advantages for handling complex systems. Bayesian optimization requires no explicit mathematical model and can efficiently optimize even when starting from a single initial point. However, as a black-box method, it demands a detailed analysis of hyperparameters, such as the acquisition function and the number of initial points, to ensure optimal performance. Thi... [more]

Recent advancements in machine learning and time series analysis have opened new avenues for improving predictive control in complex systems such as mineral flotation. Techniques leveraging multivariate predictive control in mineral flotation have seen significant progress in recent years. However, challenges in developing an accurate dynamic model that encapsulates both the pulp and froth phases have hindered further advancements. Now, with a readily available model containing equations that describe the physics of flotation froths, an opportunity for novel control strategies presents itself. In this study, a Gaussian Process (GP) Model Predictive Control (MPC) strategy is proposed to integrate uncertainty quantification directly into the control framework. By leveraging the probabilistic nature of GP models, this approach captures process variability and adapts dynamically to new data, ensuring continuous refinement of the GP model within the MPC strategy. Unlike previous implementat... [more]

Microfluidic mixing has gained popularity in the Pharmaceutical Industry due to its application in the field of Nano-based Drug Delivery Systems (DDS). The flow conditions in Microfluidic mixers enable very efficient mixing conditions, which are crucial for the production of Nanoparticles by Flash Nanoprecipitation (FNP), as it enables reproducible production of particles with low-size variability. Mixer geometry is one of the most determinant factors, as it largely determines the flow patterns and the degree of contact between the two mixing streams. In this paper, a shape optimization methodology using Computational Fluid Dynamics (CFD) and Bayesian optimization is applied to the toroidal micromixer design, considering three different operating conditions. It consists of first defining a geometry solution space and then using Multi-Objective Bayesian optimization to explore the different designs. Mixer performance is evaluated with CFD simulations and two objective functions are cons... [more]

Due to the intermittent nature of most renewable energy sources, developing good and flexible control structures for green electrolysis systems is crucial for maintaining efficient and safe plant operation. This work uses the “top-down” section of Skogestad’s plantwide control procedure to propose a suitable control architecture for PEM electrolysis systems based on advanced regulatory control. Advanced regulatory control structures, such as active constraint control, may offer several advantages over MPC and AI-based control methods as they are computationally less expensive, less affected by model accuracy, easier to scale, and offer fast disturbance rejection. In our approach, we first mapped the constraint regions for the system. Then, we reduce the complexity by reformulating the optimization problem slightly, to remove some constraint regions to obtain a simpler solution structure that gives a negligible loss. Finally, we propose an active constraint control architecture using PI... [more]

Liquid-liquid extraction (LLX) is an essential technique for separating heat-sensitive, highly diluted, or azeotropic mixtures. However, the design and optimization of LLX processes can be challenging due to mass transfer limitations and complex fluid dynamics. While distillation can often be modeled using equilibrium-based (EQ-based) approaches with (constant) height equivalent to theoretical stage (HETS) values, these kinetic effects can limit the applicability of EQ-based LLX models for conceptual design. Non-equilibrium (NEQ) or rate-based modeling can account for detailed mass transfer and fluid dynamics but further increases the nonlinearity and complexity of the respective optimization problems, which should account for closed-loop solvent recovery. To successfully address these complexities, we propose an integrated methodology combining NEQ-based simulation with EQ-based superstructure optimization to design a hybrid extraction-distillation process. An NEQ model is first used... [more]

Successful implementation of optimization-driven control techniques, such as model predictive control (MPC), is highly dependent on an accurate and detailed model of the process. As complexity in the system increases, linear approximation used in MPC may result in poor performance since a critical operating point is valid in only a small neighborhood of operation. To address this problem, this paper proposes a collaborative approach that combines linear and data-based models to predict state variables individually. The outputs of these models, along with constraints, are then incorporated into the MPC algorithm. For data-based process model, a multi-layered feed-forward network is used. Additionally, the offset-free technique is applied to eliminate steady-state errors resulting from model-process mismatch. To demonstrate the results, a binary distillation column process which is multivariable and inherently nonlinear is chosen as testbed. We compare the performance of the proposed met... [more]

Safe Bayesian Optimization (Safe BO) has demonstrated significant promise in enhancing data-driven optimization strategies in safety-critical settings, where model discrepancies, noisy measurements, and unknown safety constraints are prevalent. Despite these advancements, there still remains a limited understanding on the effectiveness and applicability of these Safe BO methods, particularly within process system engineering. Specifically, this study adapts and examines Safe Exploration for Optimization with Gaussian Processes (SafeOpt), Goal-oriented Safe Exploration (GoOSE), Gaussian Processes with Trust Region (GPs-TR) and Adversarially Robust Gaussian Processes (StableOpt). Methods such as SafeOpt and GoOSE face challenges in managing continuous systems due to their reliance on system discretization and together with StableOpt, lack the capability to manage multiple safety constraints. Thus, this work presents a comprehensive evaluation of state-of-the-art safe BO methods, with our... [more]

One market scope of bioindustries is the production of recombinant proteins for its application in serotherapy. However, its process's monitoring and optimization present limitations. There are different approaches to optimize bioprocess performance; one is using model-based control strategies such as Model Predictive Control (MPC). Another strategy is learning-based control, such as Reinforcement Learning (RL). In this work, an RL approach was applied to maximize the production of recombinant proteins in E. coli at the induction phase using as a control variable the substrate feed flow rate (Fin). The RL model was trained using the actor-critic Twin-Delayed Deep Deterministic (TD3) Policy Gradient agent. The reward corresponded to the maximum value of protein productivity. The environment was represented with a dynamic hybrid model. The optimization was evaluated by stages of two hours to check the protein productivity performance. Afterwards, the results were compared with an MPC app... [more]

Process systems are negatively impacted by manufacturing uncertainties, and increasingly by unknown-unknown disruptive events. To this effect, systems need to be designed with the inherent flexibility and resilience to overcome the impacts of uncertainties and disruptions respectively as it is more challenging to retrofit existing systems with such capabilities. To this end, we propose a methodology based on flexibility analysis to systematically explore the feasibility of design alternatives under parameter uncertainty and discrete disruption scenarios simultaneously. Multi-parametric programming is utilized to generate explicit relationships between design decisions and the resulting system’s ability to maintain feasible operations under uncertainty and disruptive events. We capture this ability by introducing the Combined Flexibility-Resilience Index (CFRI), which describes the likelihood that the system is feasible under the relevant uncertainty and disruption sets. With explicit f... [more]

Model-based control and optimisation strategies can play a key role in improving energy efficiency and reducing emissions into produced water re-injection facilities. However, building a model that adequately describes the plant is challenging and can also be used in online decision-making procedures. This work proposes a simple model based on a real water re-injection facility operating on the Norwegian continental shelf. The results demonstrate the model's flexibility, which could be fitted to different plant operating points while being fast to solve when embedded in optimisation problems. The developed model is expected to aid the implementation of strategies like self-optimising control and real-time optimisation on produced water re-injection facilities.

The motivation of this work is an application of a production scheduling based on Real-Time Optimization and Zone Control Nonlinear Model Predictive Controller on a liquid recovery unit of an LPG production plant. In this unit, the scheduling-relevant disturbances occur on a time scale relevant to the system dynamics; thus, we propose a novel combination of a well-known control strategies leading to a hierarchical two-layered strategy, where the lower layer employs a zone control nonlinear model predictive controller (NMPC) to define inventory setpoints while the upper layer uses real-time optimization (RTO) to determine optimal plant-wide flow rates from an economic perspective. Unlike a traditional RTO, the proposed upper-layer problem is parameterized by product demands, with a distinct optimization problem formulated for each demand scenario. Our novel approach allows for proactive mitigation of potential inventory issues by dynamically recalculating the distribution of plant produ... [more]

The optimization of shell-and-tube heat exchangers (STHEs) is critical for improving energy efficiency, reducing operational costs, and mitigating environmental impacts in industrial applications. This study evaluates the performance of the Success-Based Optimization Algorithm (SBOA), a novel metaheuristic strategy inspired by behavioral patterns in success perception, against seven established algorithms—Cuckoo Search, Differential Evolution (DE), Grey Wolf Optimization (GWO), Jaya Algorithm, Particle Swarm Optimization, Teaching-Learning Based Optimization, and Whale Optimization Algorithm—for minimizing the total annual cost (TAC) of STHE designs. Using the Bell-Delaware method, the optimization framework incorporates eleven decision variables, including geometric and operational parameters, subject to thermo-hydraulic constraints. A penalty function method enforces feasibility by dynamically adjusting constraint weights. Statistical analysis of 30 independent runs reveals that DE a... [more]

This paper focuses on the control and dynamics of an oil refinery process on an intermediate level - the flows, masses and compositions of and between units within the refining operation. It aims to elucidate optimal strategies for the routing of streams during upset events imposed on the process. A general flowsheet simulation technique including tunable controllers for flows, compositions, levels and reaction extents is incorporated in a Linear Programming model. A standard node represents a mixed receiving tank, with exit streams which can be split, converted and separated. These nodes can be inter-connected arbitrarily in the flowsheet. The method is demonstrated for the case of a planned 3-day shutdown of the catalytic cracker.

This work presents an approach to implement and assess fractional controllers in an extractive distillation system. The experimental dynamic data for an extractive distillation column is used as a case study. A strategy is developed to fit the operation data to fractional-order transfer functions. Then, the fractional controllers are designed in the Simulink environment in Matlab, tuning the controllers through a hybrid optimization approach. First, the approach uses a genetic algorithm to find an initial point, and then the solution is improved through the fmincon algorithm. According to the results of the design of fractional controllers, the sum of the square of errors is below 2.9x10-6 for perturbations in heat duty, and 1.2x10-5 for perturbations in the reflux ratio. Moreover, after controller tuning, a minimal value for ISE of 1,278.12 is obtained, which is approximately 8% lower than the value obtained for an integer-order controller.

Sustainable industrial development depends on optimizing resource and energy integration within Eco-industrial parks (EIPs), combined with stringent carbon emissions reduction policies. The main challenge is ensuring transparency, accountability, and data privacy while optimizing the conversion of raw materials and energy into valuable products and controlling emissions within EIPs. This research introduces an innovative framework to design optimized EIPs and deploy a blockchain-enabled trading platform for resources and emissions management, tackling these key issues. The proposed framework integrates EIPs with emission control policies, supported by two distinct smart contracts: one dedicated to blockchain-based resource trading and another handling financial transactions related to emission control policies, including other regulations such as income tax. The resource trading platform fosters transparency, enabling accurate tracking of material and energy flows. Furthermore, the fra... [more]

Real-world flowshop problems which are very common in the chemical industry are often difficult to solve in a reasonable time with allocation, sequencing, and lot-sizing decisions. Although great progress has been made in the last 20 years regarding MILP model formulations and solution algorithms, realistically-sized flowshop problems with resource and buffer constraints are still difficult to solve. On the other hand, discrete-event simulation (DES) allows for very detailed modelling of process plants, but lacking of optimization capabilities. Simulation Optimization (SO) combines the high-detail DES with mathematical optimization. We show that is possible to integrate MILP and DES using Benders decomposition. We explain the Benders-DES (BDES) approach with a small motivation example with makespan minimization objective and apply it to a real-world case study of a formulation plant with seven formulation and filling lines with sequencing, allocation, and lot-sizing decisions. We show... [more]