Optimization-based Process Research Articles

Optimization based process modeling of an anaerobic membrane bioreactor system: Application to swine wastewater

To maintain current levels of consumption in the economy with the dwindling supply of non-renewable material and energy, alternative resource streams more traditionally viewed as waste streams must be considered. Fermentation of high-strength wastewaters is one such pathway that allows for the recovery of energy, nitrogen, phosphorus, and carbon compounds. Anaerobic membrane bioreactors (AnMBRs) are an emerging technology that allow for the digestion of wastewater in a much smaller footprint than traditional anaerobic digesters. Adoption of this technology into industry has been limited by membrane capital and cleaning costs, but these costs may be offset through the recovery of valuable products. To evaluate the viability of AnMBR technology in the context of swine wastewater treatment, an optimization-based process model built upon Anaerobic Digestion Model No. 1 (ADM1) has been developed. Modeling results show that a swine wastewater stream provides potential for net positive energy generation from the AnMBR system in most cases. Sensitivity analyses around important variables were conducted to determine focus areas for future research into AnMBR technology and evaluate the robustness of the model to microbial variables that may change with different microbial communities.

Optimization-based process synthesis by phenomena-based building blocks and an MINLP-framework featuring structural screening

An existing approach for optimization-based process synthesis with abstracted phenomena-based building blocks (PBB) is extended by implementing it into a novel MINLP framework with structural screening. Consistency across the multilayer MINLP framework is guaranteed by creating a MathML/XML data model and subsequently exporting the code to the different program parts. The novel framework focuses both on fidelity by implementing thermodynamically sound models and on generality by employing a state-space superstructure that spans a large search space. In order to retain tractability, we insert a structural screening layer which pre-screens based on binary decision variables of the superstructure by graph- and rule-based analyses, penalizing non-physical instances without solution of the underlying MINLP. The MINLP framework is successfully applied on two challenging synthesis tasks to determine the separation of the feed streams of benzene and toluene, as well as of n-pentane, n-hexane, and n-heptane utilizing superstructures with two, respectively four PBB.

Advanced Modeling and Optimization Strategies for Process Synthesis.

This article provides a systematic review of recent progress in optimization-based process synthesis. First, we discuss multiscale modeling frameworks featuring targeting approaches, phenomena-based modeling, unit operation-based modeling, and hybrid modeling. Next, we present the expanded scope of process synthesis objectives, highlighting the considerations of sustainability and operability to assure cost-competitive production in an increasingly dynamic market with growing environmental awareness. Then, we review advances in optimization algorithms and tools, including emerging machine learning-and quantum computing-assisted approaches. We conclude by summarizing the advances in and perspectives for process synthesis strategies.

Best Fit for Common Purpose: A Multi-Stakeholder Design Optimization Methodology for Construction Management

Within traditional design processes, decisions are often made based on individualistic values and late-stage assessments conducted on a predefined set of design alternatives potentially leading to suboptimal design decisions and conflicts. The issues are further amplified by the growing complexity of construction project management where an increasing number of stakeholders are involved. To address those issues, a shift is needed towards a collaborative early stage optimization-based design process. The current optimization-based design approaches are not appropriately integrating stakeholders and their preferences in the optimization process and thereby not reflecting the real-life design and construction process. In this study, we present a pioneering multi-stakeholder design methodology combining preference function modeling theory and a priori optimization enabling stakeholders to find the group-optimal design fairly representing their preferences. The application of the developed methodology is demonstrated on a real-life multi-storey building design case. The study provides a novel approach for managing design and construction projects for academic and industry stakeholders. It also sets the foundation for the further development of stakeholder-oriented optimization-based design.

Improved invasive weed-lion optimization-based process mining of event logs

Improved invasive weed-lion optimization-based process mining of event logs

Optimization-Based Business Process Model Matching

The rapid increase in generation of business process models in the industry has raised the demand on the development of process model matching approaches. In this paper, we introduce a novel optimization-based business process model matching approach which can flexibly incorporate both the behavioral and label information of processes for the identification of correspondences between activities. Given two business process models, we achieve our goal by defining an integer linear program which maximizes the label similarities among process activities and the behavioral similarity between the process models. Our approach enables the user to determine the importance of the local label-based similarities and the global behavioral similarity of the models by offering the utilization of a predefined weighting parameter, allowing for flexibility. Moreover, extensive experimental evaluation performed on three real-world datasets points out the high accuracy of our proposal, outperforming the state of the art.

Model-based evaluation of a membrane-assisted hybrid extraction-distillation process for energy and cost-efficient purification of diluted aqueous streams

The concentration of diluted aqueous streams is an important task in bio-based production processes, as well as the recovery of valuable impurities prior to wastewater purification. Affinity-based separation processes, such as liquid-liquid extraction, are generally considered favorable to distillation, which however is regularly used for solvent recovery. To reduce the effort for a solvent-based extraction and improve the economic performance, an extended hybrid process concept is considered, which exploits energy-efficient pressure-driven membrane separations, i.e. reverse osmosis or nanofiltration, for preconcentration. The current work presents a systematic evaluation of this process for purification of a diluted aqueous γ-valerolactone stream, considering two solvents. The potential economic savings are first evaluated in a model-based assessment prior to an experimental screening of suitable membranes. Finally, a detailed economic evaluation of the processes is conducted by an optimization-based process design. The proposed approach allows to quickly identify a considerable saving potential for the membrane-assisted hybrid process.

Analog Gross Fault Identification in RF Circuits Using Neural Models and Constrained Parameter Extraction

The demand and relevance of efficient analog fault diagnosis methods for modern RF and microwave-integrated circuits increase with the growing need and complexity of analog and mixed-signal circuitry. The well-established digital fault diagnosis methods are insufficient for analog circuitry due to the intrinsic complexity in analog faults and their corresponding identification process. In this paper, we present an artificial neural network (ANN) modeling approach to efficiently emulate the injection of analog faults in RF circuits. The resulting metamodel is used for fault identification by applying an optimization-based process using a constrained parameter extraction formulation. A generalized neural modeling formulation to include auxiliary measurements in the circuit is proposed. This generalized formulation significantly increases the uniqueness of the faults identification process. The proposed methodology is illustrated by two faulty analog circuits: a CMOS RF voltage amplifier and a reconfigurable bandpass microstrip filter.

A Multiobjective Optimization Including Results of Life Cycle Assessment in Developing Biorenewables-Based Processes.

A decision support tool has been developed that uses global multiobjective optimization based on 1) the environmental impacts, evaluated within the framework of full life cycle assessment; and 2) process costs, evaluated by using rigorous process models. This approach is particularly useful in developing biorenewable-based energy solutions and chemicals manufacturing, for which multiple criteria must be evaluated and optimization-based decision-making processes are particularly attractive. The framework is demonstrated by using a case study of the conversion of terpenes derived from biowaste feedstocks into reactive intermediates. A two-step chemical conversion/separation sequence was implemented as a rigorous process model and combined with a life cycle model. A life cycle inventory for crude sulfate turpentine was developed, as well as a conceptual process of its separation into pure terpene feedstocks. The performed single- and multiobjective optimizations demonstrate the functionality of the optimization-based process development and illustrate the approach. The most significant advance is the ability to perform multiobjective global optimization, resulting in identification of a region of Pareto-optimal solutions.

Recent Developments and Challenges in Optimization-Based Process Synthesis

This article first reviews recent developments in process synthesis and discusses some of the major challenges in the theory and practice in this area. Next, the article reviews key concepts in optimization-based conceptual design, namely superstructure representations, multilevel models, optimization methods, and modeling environments. A brief review of the synthesis of major subsystems and flowsheets is presented. Finally, the article closes with a critical assessment and future research challenges for the process synthesis area.

A Variational Art Algorithm for Image Generation

The authors propose a variational art algorithm: a virtual system-based optimization algorithm developed for generating images. Observing that the topology optimization method used for multiphysics system design can produce two- or three-dimensional layouts without baselines, the authors propose to expand it beyond engineering applications for generating images. They have devised a virtual physical system—a heat-path system—that “interprets” the optimization-based process of image generation as the simultaneous drawing of multiple strokes in a painting.

Biomass-Based Production of Benzene, Toluene, and Xylenes via Methanol: Process Synthesis and Deterministic Global Optimization

The pursuit toward an environmentally sustainable energy landscape requires the development of economically competitive renewable processes. Efficient utilization of renewable resources is an important first step toward meeting this goal. To this extent, we introduce a systematic deterministic global optimization-based process synthesis framework that determines the most profitable processes to produce benzene, toluene, and/or xylenes from biomass via methanol. Our framework incorporates several novel, competing, and/or commercial technologies. We quantify the effect that biomass type has on the overall profit of a refinery by investigating forest residues, agricultural residues, and perennial crops as potential feedstocks. A thorough economic analysis, together with material, energy, carbon, and greenhouse gas balances, are provided for every proposed process design. The capability of our proposed approach is illustrated through several case studies that produce varying ratios of p-, o-, and m-xylene acr...

Coal and Biomass to Liquid Transportation Fuels: Process Synthesis and Global Optimization Strategies

The thermochemical conversion of coal and biomass to liquid transportation fuels from a synthesis gas intermediate is investigated using an optimization-based process synthesis framework. Two distinct types of coal (LV bituminous and coal commonly found in the province of Anhui, China) and two types of biomass (hardwood and duckweed) are considered as feedstocks. The superstructure incorporates alternative conversion pathways of synthesis gas which include methanol formation and conversion into FischerTropsch hydrocarbons. Methanol may be converted to gasoline or olefins, and the olefins may be subsequently converted to gasoline and distillate. A rigorous deterministic global optimization branch-and-bound framework is utilized to determine the optimal process topology that produces liquid fuels at the lowest possible cost. Economies of scale are evident as the refinery capacity increases and it is observed that the fuel ratios of the final liquid products have a significant impact on the optimal topology of the plant. The results suggest that liquid fuels production from coal and biomass can be competitive with petroleum-based processes.

Drive optimization of a pulsatile total artificial heart

Purpose – Total artificial hearts (TAHs) are required for the treatment of cardiovascular diseases. In order to replace the native heart a TAH must provide a sufficient perfusion of the human body, prevent blood damage and meet the implantation constraints. Until today there is no TAH on the market which meets all constraints. So the purpose of this paper is to design a drive in such a way that the operated TAH meets all predefined constraints. Design/methodology/approach – The drive is designed in terms of weight and electric losses. In setting up a cost function containing those constraints, the drive design can be included in a optimization process. When reaching the global minimum of the cost function the optimum drive design is found. In this paper the optimization methods manual parameter variation and differential evolution are applied. Findings – At the end of the optimization process the drive's weight amounts to 460 g and its mean losses sum up to 10 W. This design meets all predefined constraints. Further it is proposed to start the optimization process with a parameter variation to reduce the amount of optimization parameters for the time consuming differential evolution algorithm. Practical implications – This TAH has the potential to provide a therapy for all patients suffering from cardiovascular diseases as it is independent of donor organs. Originality/value – The optimization-based design process yields an optimum drive for a TAH in terms of weight and electrical losses. In this way a TAH is developed which meets all implantation constraints and provides sufficient perfusion of the human body at the same time.

Biomass and Natural Gas to Liquid Transportation Fuels: Process Synthesis, Global Optimization, and Topology Analysis

An optimization-based process synthesis framework is proposed for the thermochemical conversion of biomass and natural gas to liquid fuels (BGTL). Hydrocarbons are produced from synthesis gas either directly via Fischer–Tropsch synthesis or indirectly via catalytic conversion of methanol over ZSM-5. Different conversion technologies are investigated to examine their economic effects on BGTL refineries that produce gasoline, diesel, and kerosene. The process synthesis framework includes simultaneous heat, power, and water integration and utilizes a rigorous deterministic global optimization strategy to mathematically guarantee the minimal cost of the BGTL refineries. The refineries have at least 50% less life-cycle CO2 emissions than a standard petroleum-based refinery. Forty-eight case studies are presented to determine the effect of refinery capacity, liquid fuel composition, and biomass feedstock on the overall cost, topological design, material/energy balances, and life-cycle greenhouse gas emissions. Results suggest that these systems can be economically competitive with petroleum-based processes while achieving the 50% emissions reduction.

Biomass to liquid transportation fuels (BTL) systems: process synthesis and global optimization framework

An optimization-based process synthesis framework is proposed for the thermochemical conversion of biomass to liquid fuels (BTL). Gasification of biomass is used to generate synthesis gas which can be converted to raw hydrocarbons either directly using Fischer–Tropsch synthesis or indirectly using catalytic conversion of methanol over ZSM-5. Multiple technologies are considered for generation of the raw hydrocarbons including (i) six types of Fischer–Tropsch units with different temperatures, catalyst types, and hydrocarbon effluent compositions, (ii) methanol conversion using methanol-to-gasoline, and (iii) methanol conversion using methanol-to-olefins. The hydrocarbons are upgraded into the final liquid fuel products (i.e., gasoline, diesel, and kerosene) using one or more technologies including ZSM-5 catalytic conversion, oligomerization, hydrocracking, isomerization, alkylation, and hydrotreating. A simultaneous heat, power, and water integration is included within the process synthesis framework to directly examine the costs associated with utility production and wastewater treatment with a particular topological design. A rigorous global optimization branch-and-bound strategy is implemented to mathematically guarantee the development of a BTL refinery that is economically and environmentally superior to all competing designs. Twenty-four case studies are investigated to determine the effect of refinery capacity, liquid fuel composition, and biomass feedstock on the overall system cost, the BTL refinery topological design, the process material/energy balances, and the lifecycle greenhouse gas emissions.

Incorporating Adjustable Features in the Optimal Design of Polymer Sheet Extrusion Dies

It is common for materials processing operations to have adjustable features that may be used to improve the quality of the final product when variability in operating conditions is encountered. This paper considers the polymer sheeting die design problem where variability in operating temperature or material properties, for example, requires that the die be designed to perform well under multiple operating conditions. An optimization procedure is presented where the design variables parametrize both stationary and adjustable model variables. In this approach, adjustable features of the die cavity are modified in an optimal manner consistent with the overall design objectives. The computational design approach incorporates finite element simulations based on the Generalized Hele-Shaw approximation to evaluate the die’s performance measures, and includes a gradient-based optimization algorithm and analytical design sensitivities to update the die’s geometry. Examples are provided to illustrate the design methodology where die cavities are designed to accommodate multiple materials, multiple flow rates, and various temperatures. This paper demonstrates that improved tooling designs may be computed with an optimization-based process design approach that incorporates the effect of adjustable features.

Control of an alternating aerobic–anoxic activated sludge system — Part 1: development of a linearization-based modeling approach

A two-phase model (linear in each phase) is developed for an alternating aerobic–anoxic completely mixed activated sludge process for the removal of carbonaceous substrates, nitrification, and denitrification. The linearized model is adapted from the activated sludge model No. 1 (ASM1) originally developed by the International Association on Water Pollution Research and Control. The modified dynamic model captures the essential process features of ASM1 while dramatically reducing demand on computational resources. Simulations may be conducted in such short times that the simplified model is suitable for inclusion in on-line optimization-based process control schemes. Calibrated model parameters are within the ranges specified in ASM1. Comparison of model predictions with experimental measurements from two different sets of bench-scale alternating aerobic–anoxic reactors indicates reasonable prediction accuracy of the model; further compensation for inaccuracy is achievable by introducing a feedback loop.