Process Flowsheet Research Articles

Immobilized Cell Bioreactor Industrialization in the Development of an Innovative Optical Biosensor Technology

This commentary shows the development of a new optical biosensor, based on cell immobilization of Pseudomonas Fluorescens HK44, in nano and micro-tubular cellulose (TC) and a mixture of carbohydrate nanotubes (CHNTs) and carbohydrate micro-tubes (CHMTs). Methodology follows, this biocatalyst can be industrialized with the use of a single tank immobilized cell bioreactor (ICB). A techno-economic analysis was conducted within the framework of it by designing a process flow sheet with mass and energy balance. According to its case study, the investment is 227,800 euros, and the daily production cost is 1434 euros, with a maximum daily added value of 25,000 euros. The discussion revealed that novel research proposals and a novel study concept are being developed in the field of biosensors. The results are supported by papers published on ICB area development. The problem that leads to this commentary is industrialization of ICB, in the case of a simple biosensor development using immobilized cells and it is the objective.

Recovery of Tellurium from Waste Anode Slime Containing High Copper and High Tellurium of Copper Refineries

Tellurium is used in cadmium tellurium-based solar cells. Mercury cadmium telluride is used as a sensing material for thermal imaging devices. High-purity tellurium is used in alloys for electronic applications. It is one of the important raw materials for solar energy applications. It is used as an alloying element in the production of low-carbon steel and copper alloys. Tellurium catalysts are used chiefly for the oxidation of organic compounds and as vulcanizing/accelerating agents in the processing of rubber compounds. Even though several researchers tried to recover tellurium from different raw materials, there is no attempt to develop a process flow sheet to recover tellurium from waste anode slime having a high tellurium concentration. In this study, optimum conditions were developed to recover Te and Cu from anode slime with the composition Cu: 31.8%, Te: 24.7%, and As: 0.96%. The unit operations involved are leaching, purification, and electro winning. The optimum conditions for producing Te at a recovery of 90% are found to be roasting of anode slime at 450 °C without the addition of soda ash followed by leaching in 1 M NaOH at 10% pulp density for 2 h. The purity of Te metal achieved was up to 99.99%, which could provide a sustainable energy future. The major impurities of the tellurium are observed to be in the order: Se > Sb > As > Cu.

Establishment of a Hydrometallurgical Scheme for the Recovery of Copper, Nickel, and Cobalt from Smelter Slag and Its Economic Evaluation

In pursuit of carbon neutrality, the demand for metals that are necessary for the development of clean energy technologies is rapidly increasing. Metallurgical waste, such as slag, presents a promising secondary source of these key metals. This research aims to develop an eco-friendly hydrometallurgical process to recover Cu, Ni, and Co from discarded copper/nickel slag. High-pressure acid leaching (HPAL) was used to selectively leach Ni, Cu, and Co from the fayalite slag, yielding high leaching efficiencies of 99.9%, 89.4%, and 99.9%, respectively, with low Fe and Si tenors to the pregnant leach solution (PLS). The solvent extraction (SX) technique utilizing LIX 984N was used to selectively extract and enrich copper from the dilute PLS to about 23 g/L Cu with a very low Fe concentration of 0.05 g/L. Potassium amyl xanthate (PAX) solution was used to form Ni and Co xanthate complexes from the raffinate solution. Nickel was selectively recovered using ammonia solution, while the cobalt xanthate complex was thermally decomposed and recovered as cobalt oxide solids of about 25 wt.% Co. A comprehensive process flowsheet is presented. Furthermore, to realize the real application of the developed slag cleaning process, a preliminary economic evaluation was performed.

Liquid–Liquid Equilibria of Butyric Acid, Water, and Methyl Isobutyl Ketone with the Aid of Biological Buffer as a Green Auxiliary Agent

Biological buffer has a significant impact on aqueous organic phase equilibria in extraction processes. This study investigated the effect of biological buffer on the efficacy of butyric acid extraction from aqueous solution utilizing methyl isobutyl ketone (MIBK) as the extraction solvent. The liquid–liquid equilibria (LLE) of quaternary systems of methyl isobutyl ketone (MIBK) + butyric acid + water + biological buffer MOPS [3-(N)-morpholino propane sulfonic acid], HEPES (2-(4-(2-hydroxyethyl)-1-piperazinyl)ethanesulfonic acid), or EPPS (4-(2-hydroxyethyl)-1-piperazine propanesulfonic acid) were determined at temperature T = 303.15 K (30 °C) and P = 0.1 MPa (atmospheric pressure). The addition of EPPS to the butyric acid aqueous solution resulted in superior extraction efficacy compared to the other biological buffers, as determined by experimental data. Generally, the extraction performance was in the order EPPS > MOPS > HEPES, which represented the buffering-out strength as well. The non-random two-liquid (NRTL) and UNIversal QUAsi Chemical (UNIQUAC) models fit well with the actual LLE data of the quaternary systems. The σ-profile analysis was also evaluated in this study. Additionally, a potential process flow sheet using biological buffer and MIBK solvent was suggested for removing butyric acid from its aqueous solution.

Potential of low-grade fluorite ore of Landi Kotal, Khyber Pakhtunkhwa Province, Pakistan

This paper describes the results of a beneficiation study conducted on low-grade fluorite ore. The study was aimed at the development of a suitable beneficiation process to produce chemical grade fluorite concentrate. The representative sample of the ore was up-graded by leaching and flotation process. The important parameters of froth flotation process were optimized to obtain highest grade and recovery of fluorite concentrate. A single-factor variation method was applied to get the optimum conditions of the process. The rougher concentrate was reground to liberate fine locked fluorite particles and two cleaning flotation were employed in close circuit to achieve higher grade concentrate. A process flowsheet was designed in the light of these tests. After optimization of process parameters, the fluorite ore containing 62.24% CaF2 could be upgraded to a concentrate assaying 97.55% CaF2 with an overall recovery of 92.73%. The prepared fluorite concentrate meets the specifications of chemical grade and is suitable for production of fluoride based chemicals. Bangladesh J. Sci. Ind. Res. 58(2), 107-118, 2023

Novel intensified process for di-isopropyl ether production from isopropanol etherification using reactive distillation technology

The preparation of di-isopropyl ether (DIPE), as an important chemical solvent, by isopropanol (IPA) etherification is limited by chemical equilibrium and azeotropic formation in its production processes. A novel sustainable etherification process based on reactive distillation (RD) coupled with extractive distillation is proposed. The reaction of IPA etherification was carried out by cation exchange resin NKC-9 in a 250 mL autoclave and fitted by a pseudo-homogeneous model. According to the reaction kinetic, the IPA etherification RD model was established. To confirm the viability of RD and the accuracy of the model, pilot-scale RD experiments for generating DIPE were investigated. Based on the established model matching the pilot scale experiments, the operating conditions of the etherification RD column were optimized by sensitivity analysis. The simulation results show that the conversion of IPA in the RD column can be increased from 18.2 % to 77.9 % compared with autoclave and the yield of DIPE in the whole process flowsheet can reach 99.9 %, which demonstrates that combining reactive distillation with extractive distillation technology is a feasible and advantageous solution for DIPE production.

Assessing the future of second-generation bioethanol by 2030 – A techno-economic assessment integrating technology learning curves

Lignocellulosic biomass is the most abundant source of renewable biomass and is seen as a high-potential replacement for petroleum-based resources. The conversion technologies to advanced biofuels are still at a low maturity level, thus allowing for future cost reductions through technological learning. This fact is barely considered in state-of-the-art techno-economic assessments and a structured approach to account for technological learning in techno-economic assessments is needed. In this study, a framework for techno-economic assessments of advanced biofuels, integrating learning curves, is proposed. As a validation of this framework, the economic feasibility of the valorization of corn stover for the production of second-generation bioethanol in Belgium is studied. Process flowsheet simulations in Aspen Plus are developed, with an emphasis on the comparison of four different pretreatment technologies and two plant capacities at 156 dry kt biomass/y and 667 dry kt/y. The dilute acid pretreatment model of the large-scale biorefinery required the lowest minimum learning rate to reach an economically feasible biorefinery by 2030, being 3.9%, almost half as the one calculated for the smaller scale plant. This learning rate seems to be achievable based on learning rates commonly estimated in literature. We conclude that there is a potential for advanced ethanol production in Belgium under the current state of technology for large-scale biorefineries, which require additional biomass imports, when accounting for future cost reductions through learning.

An integrated framework for sustainable process design by hybrid and intensified equipment

With the increasing concern of the competitive market and carbon emissions, a sustainable, efficient process design becomes more and more important. The task of sustainable process design is to find efficient configurations and operating parameters to produce products or utilize materials. Currently, the scientific community's major focus is on separation synthesis, which aims to identify the best downstream separation configurations through different methods such as optimization, heuristic, and hybrid methods. Besides, innovative separation techniques with lower capital/operating costs and carbon emissions were proposed and identified to further improve the downstream process performance. However, most of the separations synthesis methods were based on conventional unit operations and usually applied for generating sustainable processes with a given reaction pathway without considering the possibilities of different raw materials. Generally, to produce a given target product, multiple reaction pathways are available, and each reaction pathway leads to different downstream processes. Also, applying intensification in the early stages of process design, e.g., separation synthesis, can generate sustainable process flowsheets with higher energy efficiency and lower environmental impact. This work aims to develop an integrated process synthesis framework, which includes reaction synthesis for the reaction pathways selection and separation synthesis involving intensified/hybrid units. So, for the new process design, given the product one needs to produce, this framework could identify the optimal reaction pathway with its best separation route. The developed framework was used to solve three case studies, including the new process design of dimethyl carbonate (DMC) production and retrofitting design of cumene and styrene production. For all the case studies, the framework was able to generate multiple innovative solutions that are sustainable and energy-efficient.

Carbohydrate nanotubes production and its techno-economic validation

The production of carbohydrate nanotubes (CHNTs) using agricultural wastes is proposed in this investigation. The corncob was found to be the most productive for our purpose among the four lignocellulosic raw materials tested. CHNTs production was accomplished in two stages. Tubular cellulose (TC) was prepared from raw substrates through a delignification process, and the prepared tubes of TC were cut into nano-size carbohydrate tubes in a chemical-free process. To achieve this, cellulase was produced in our lab using agricultural residue, employing the non-pathogenic fungus Trichoderma reesei, a high cellulase producer. Analysis of the produced CHNTs proved stability, nano-dimension lengths, and increased crystallinity. The technoeconomic feasibility report showed that the production of CHNTs is cost-effective. This was supported by a process flow sheet with mass and energy balances based on laboratory experimental results.

IMPROVEMENT OF MINING AND PROCESSING FLOWSHEETS AT STRUCTURALLY COMPLEX ORE DEPOSITS

IMPROVEMENT OF MINING AND PROCESSING FLOWSHEETS AT STRUCTURALLY COMPLEX ORE DEPOSITS

SFILES 2.0: an extended text-based flowsheet representation

SFILES are a text-based notation for chemical process flowsheets. They were originally proposed by d’Anterroches (Process flow sheet generation & design through a group contribution approach) who was inspired by the text-based SMILES notation for molecules. The text-based format has several advantages compared to flowsheet images regarding the storage format, computational accessibility, and eventually for data analysis and processing. However, the original SFILES version cannot describe essential flowsheet configurations unambiguously, such as the distinction between top and bottom products. Neither is it capable of describing the control structure required for the safe and reliable operation of chemical processes. Also, there is no publicly available software for decoding or encoding chemical process topologies to SFILES. We propose the SFILES 2.0 with a complete description of the extended notation and naming conventions. Additionally, we provide open-source software for the automated conversion between flowsheet graphs and SFILES 2.0 strings. This way, we hope to encourage researchers and engineers to publish their flowsheet topologies as SFILES 2.0 strings. The ultimate goal is to set the standards for creating a FAIR database of chemical process flowsheets, which would be of great value for future data analysis and processing.

Algebraic surrogate‐based process optimization using Bayesian symbolic learning

AbstractHere, we propose a strategy for the global optimization of process flowsheets, a fundamental problem in process systems engineering, based on algebraic surrogates that are built from rigorous simulations via Bayesian symbolic regression. The applied method provides a closed‐form expression that can be optimized to global optimality using state‐of‐the‐art solvers, where BARON or ANTIGONE were the solvers of choice. When predicting unseen test data, the algebraic models show a similar accuracy level compared to traditional surrogates based on Gaussian processes. However, they can be more easily optimized to global optimality due to their analytical closed‐form structure, which allows the user to apply well‐established global deterministic solvers. We show the capabilities of our approach in several case studies, ranging from process units to full flowsheets. The performance of our approach is assessed by comparing the CPU time for model building, the prediction accuracy of the identified model, and the CPU time for the subsequent optimization with a proven benchmark.

Improvement of Mining and Processing Flowsheets at Structurally Complex Ore Deposits

Improvement of Mining and Processing Flowsheets at Structurally Complex Ore Deposits

Investigations into the closed-loop hydrometallurgical process for heavy metals removal and recovery from biosolids via mild acid pre-treatment

Biosolids contain heavy metals (HMs), restricting their beneficial reuse in agricultural land. However, these metals can be a valuable resource in many applications if recovered efficiently. Therefore, the removal and recovery of HMs and other limiting contaminants in biosolids without degrading the organic nutrients of the resulting treated biosolids demands holistic investigations. A closed-loop hydrometallurgical treatment process for metal removal and recovery from biosolids was developed in this study. Firstly, mild acid treatment using 3% v/v H2SO4 at 25 °C, 600 rpm for 30 min was performed in a 1 L continuous stirred tank reactor to extract common HMs (such as As, Cd, Co, Cr, Cu, Ni, Pb, and Zn) from biosolids into the aqueous phase. The effects of solids concentration and acid types on the HMs extraction efficiency were studied. Then, the primary acid leachate stream was continuously recycled for metal extraction from biosolids until the dissolved metals in the solution reached saturation concentration. After that, the dissolved metals were recovered in staged NaOH precipitation and adsorption. Low solids contents (<5% w/v) using mineral acids having pH <2 and oxidation-reduction potential (ORP) ∼500 mV (versus SHE) favoured HMs solubilisation from biosolids with an average extraction efficiency of 70%. The dissolution of ferric iron (Fe3+) by H2SO4 and subsequent in-situ formation of ferric sulfate enhanced the metal extraction strength of the spent leachate stream during recycling. However, the solids loading in each leaching process must be kept low to prevent ferric concentration build-up and precipitation as the leachate pH steadily increases above 2 during recycling. Amongst the metal recovery methods investigated, H2O2 oxidation prior to 2-stage NaOH precipitation had the highest efficiency with 75–95% HMs recovery. The clarified stream was used to neutralise the acidic treated biosolids to close the process loop. The complete process flowsheet was developed with mass balances, and the fate of nutrients (mainly C, N, and P) and major per- and polyfluoro alkyl substances (PFAS) were overviewed.

Removal of impurity Metals as Phosphates from Lithium-ion Battery leachates

Recovery of critical materials from end-of life (EOL) lithium-ion batteries (LIB) is gaining interest as demands for materials grows. Hydrometallurgical processes start with an intermediate product known as black mass which contains critical materials of interest (Co, Ni, Li, and graphite) as well as impurities such as Cu, Al, and Fe. These impurities are deleterious to downstream separation processes, as well as impact functionality of the final products. These impurities effectively compete with most solvent extraction (SX) and metal recovery processes to diminish the overall yields of the desired materials. In this work, a process flowsheet is presented where a previously reported electrochemical leaching (ECL) process is followed by selective precipitation using diammonium hydrogen phosphate (DAP) to remove Cu, Al, and Fe from LIB leachate solutions. The electrochemical leach process removes Cu and produces a pH-adjusted leachate, ca. pH 2, without requiring an extra Cu extraction step. The addition of the precipitant DAP to leachate by slightly adjusting the pH to 3–4 at 45 °C, precipitates 95–99% of Al and Fe as their phosphates and the leachate retains >95% of Co, Ni and Li. After the filtration of phosphate impurities, the solution is ready for further processing, such as SX. As an unexpected result from the leachate processing, 20–30% of the Ni and Co co-crystalize as the double salt after cooling to room temperature, which could provide a shorter route to their recovery.

Modeling and Experimental Validation for the Preparation of Concentrated Tea Extract Using a Forward Osmosis Process Using a Food-Grade Inorganic Draw Solute

In this study, a one-dimensional mathematical model was developed for the concentration of tea extract using a forward osmosis (FO) process. The membrane transport parameters were estimated by minimizing the error between the model output and the experimental results. The model equations were solved to predict the FO performance within allowable error limits. Further, a series of process flow sheet simulation studies were performed to investigate the effect of different operating conditions on the overall FO performance. Based on the simulation results, a multicriteria optimization was developed to attain the maximum permeate and minimum specific reverse solute flux (SRSF). Using the best-performing process flow sheet model, the feasibility of seawater and high-concentration reject brine as a draw solution was also investigated for the concentration of tea extract. The high-concentration reject brine (105.17 g L–1) can result in rapid dewatering with minimal SRSF (0.14 g L–1) along with simultaneous seawater dilution (81.38 g L–1).

Process flowsheet development for selective arsenic removal, lead and antimony recovery from lead softening slag

ABSTRACT A pyro-hydrometallurgical process is proposed in this study for the selective arsenic removal and the Pb and Sb recovery from lead softening slag, which incorporates NaOH roasting followed by water leaching, carbothermic reduction, and HNO3 leaching as process steps. Lead softening slag, containing 63.9 wt% Pb, 17.7 wt% Sb, and 4.4 wt% As, is first roasted with NaOH at 400°C, followed by water leaching at room temperature. The effect of the roasting temperature and NaOH dosage is investigated with regard to the removal yield of As, Pb, and Sb. The arsenic-free slag is reduced with carbon for Pb recovery as the antimonial lead. The reduction temperature and carbon dosage are optimized by applying FactSage modeling and performing experimental tests for selective Pb recovery. The residual slag is leached with HNO3 solution for the further removal of Pb and enrichment of Sb. The chemical and phase composition of the final leaching residue is determined to assess the possibility of using it as a feedstock for antimony trioxide production. The proposed process flowsheet enables the recovery of Pb and Sb as antimonial lead and antimony trioxide, respectively.

Reducing the cost of low-carbon hydrogen production via emerging chemical looping process

A thorough techno-economic analysis where inherent carbon capture is examined against state-of-the-art blue hydrogen production configurations for large (100,000 Nm3/h) and very large (333,000 Nm3/h) capacities. Advanced solvent-based technologies based on post-combustion capture and auto-thermal reformer combined with a gas heated reformer are simulated with process flowsheet software and compared with the emerging chemical looping process. A network of dynamically operated packed bed reactors has been designed and modelled using an in-house code and key parameters generating uncertainties in the results have been examined in a sensitivity analysis. The chemical looping reforming process presents a higher net reforming efficiency than the benchmark cases (8.2 % higher at large scale and 1.5 % higher at very large scale) ranged 75.4–75.7 % while the specific energy for CO2 avoidance is negative in the range of −0.78 to −0.85 MJ/kgCO2. In the carbon capture cases, the chemical looping reforming in packed beds technology generated a levelised cost of hydrogen of 168.9 £/kNm3H2 for the large scale and 159.1 £/kNm3H2 for the very large scale, with the values for the benchmark cases being higher at 196.4 and 166.6 £/kNm3H2, respectively while the levelised cost of hydrogen values are 1 % higher in the benchmark cases where carbon emission price is accounted for. The carbon capture ratio is 99.9 % for the chemical looping reforming cases compared to 90–91 % for the benchmark ones, thus providing a significant foreground for the scale-up and implementation of chemical looping reforming technologies for hydrogen production.

Application of Alarm Correlations in Root Cause Diagnosis of Plant-wide Oscillations*

Plant-wide oscillations are common types of disturbances in industrial processes, whose effects propagate to multiple process units through material and/or information flow paths. Such oscillations cause poor controller performance and thus can impact product quality and overall plant performance. Early detection of oscillations and root cause diagnosis is essential. Constructing an adjacency matrix is an efficient method to locate the root cause of oscillations and uses control loop digraphs to capture controller interactions from the process flowsheet. However, constructing an adjacency matrix is time-consuming and resource-intensive, especially when dealing with complex industrial facilities. This paper proposes an alternative data-driven method to construct an adjacency matrix using alarm correlations extracted from historical Alarm & Event (A&E) data. A component-level adjacency matrix is constructed by consolidating process unit information from alarm configuration attributes. Potential root causes of oscillations are shortlisted for further investigations using a process knowledge-based method. This preliminary diagnosis tool can reduce the computational complexity of the subsequent knowledge-driven stage. The applicability and effectiveness of the proposed method are demonstrated via a case study using a benchmark industrial dataset.

A coupled reinforcement learning and IDAES process modeling framework for automated conceptual design of energy and chemical systems

This study introduces an advanced automated system for designing diverse chemical or electrochemical systems, requiring minimal user expertise, and enabling designing and optimization from scratch.