Batch Plants Research Articles

Removal of organic micropollutants from municipal wastewater by powdered activated carbon - activated sludge treatment

In Powdered Activated Carbon (PAC)-Activated Sludge (AS) Treatment, PAC is directly dosed into the AS tank. In this study, batch and pilot plant experiments were carried out to investigate the efficacy of PAC-AS treatment concerning the removal of organic micropollutants (OMPs). Batch and pilot plant experiments showed comparable removal efficiency of nine OMPs: a mean removal of 80 % could be achieved at a specific PAC dose of 2.5 mg PAC/mg DOC; during a PAC cut-off experiment, the removal performance leveled off in the first 6 h without PAC dosage. A further investigation involving an intermittent dosage of PAC demonstrated that OMPs were removed stably when PAC was dosed every 6 h, which implies the possibility of reduced investment and maintenance costs of a dosing unit.DOC was removed by 18 ± 4 % when the PAC doses ranged from 1.9 to 2.3 mg PAC/mg DOC in this study. A more detailed characterization of the organic matter was performed by means of size exclusion chromatography coupled with organic carbon detection (SEC-OCD). The high molecular weight fraction (F1) and hydrophobic organic compounds (DOCh) were most adsorbed by PAC, revealed by batch and pilot plant experiments. It indicates a higher competition potential of these two fractions against OMP adsorption compared to other DOC fractions in the PAC-AS system.

Evaluation of the effect of pressure and heat transfer on the efficiency of a batch fuel reactor, using Iron-based Oxygen Carrier with a CFD model

Coupling a Chemical Looping Combustor fed with biofuels with a turbo expander is a promising Negative Emissions Technology (NET) to realize climate neutral targets in China and Europe. This is also an example of Bioenergy with Carbon Capture and Storage (BECCS) technology. To realize it, we need a Pressurized Chemical Looping Combustion process (PCLC). In this work, a Eulerian-Lagrangian hybrid model is developed in Barracuda-VRTM software, incorporating chemical reactions to predict the performance of a Fuel Reactor using Fe2O3 as oxygen carrier and syngas as fuel, under different pressures, ranging from 1 bar to 20 bars. The model predicted the conversion efficiency of syngas reduction using an iron-based oxygen carrier (Fe2O3/Al2O3). The results show, that the increase in pressure promotes the conversion of CO and inhibits the conversion of H2. When the two gases are considered together, the increase in pressure promotes the reaction between syngas and Fe2O3 and reduces the demand for Fe2O3 oxygen carrier per unit of syngas Lower Heating Value and so also the inventory of the reactor. Increasing temperatures promotes both the reaction of H2 and CO with Fe2O3. Dealing with CO conversion, this is more affected by pressure changes and temperature changes than H2. This represents important information for Fuel Reactor design, scale up and optimization. Further validation is neded in batch and continuous pressurised plants.

Novel Multiple Time-grid Continuous-time Mathematical Formulation for Short-term Scheduling of Multipurpose Batch Plants.

In this work, we have developed two novel unit-specific event-based mixed-integer linear programing models for scheduling multipurpose batch plants. The concept of indirect and direct material transfer is introduced to rigorously sequence and align tasks in different units. A batch after production is allowed to be partially transferred to storage and downstream processing units or held in processing units over multiple event points. The computational results demonstrate that the proposed models require a smaller number of event points in many cases to achieve optimality than existing unit-specific event-based models. It is interesting to find that no task is required to span over multiple event points to reach optimality for all addressed examples. The best variant developed is superior to existing unit-specific event-based models with the same or better objective values by a maximum improvement of 67%. The computational effort is significantly reduced by at least 1 order of magnitude in some cases.

Optimization of the production and quality of biogas in the anaerobic digestion of different types of biomass in a batch laboratory biodigester and pilot plant: Numerical modeling, kinetic study and hydrogen potential

The study aims to evaluate the biogas production and quality from four biomasses (microalgae (MB), sorghum (S), corn stubble (CS), rapeseed oil (RO)) in a digestion process carried out in two batch reactor (6 L) and pilot plant (1.5 m3) agitated mechanically.The substrates were characterized and anaerobic digestion was carried out as batch tests in mesophilic conditions for 30–35 days. Inoculum/substrate ratio was 1:1–2:1. Gas composition and total gas volume produced were monitored. Methane yields of 306, 345, 419, and 740 NL kg VS−1 were obtained for MB, CS, S, and RO, respectively, in laboratory tests, while in pilot plant tests were 182, 151, 397 and 655 NL kg VS−1. CH4 percentage in biogas was 49–60%. The yield of H2 generated for the four biomasses in the two types of biodigesters has been estimated, obtaining values between 16 and 39 mL g VS−1.First-order, Modified Gompertz, and Cone models have been applied to evaluate the kinetic parameters on the methane produced in the batch and pilot plant tests, obtaining an excellent fit. ADM1 model with 19 biological processes (disintegration of biomass composite, enzymatic hydrolysis, and digestion of soluble materials mediated by organisms), acid-base equilibria, kinetic study, and liquid-gas transference has been used to fit the cumulative methane volume.

Machine Learning and Inverse Optimization for Estimation of Weighting Factors in Multi-Objective Production Scheduling Problems

In recent years, scheduling optimization has been utilized in production systems. To construct a suitable mathematical model of a production scheduling problem, modeling techniques that can automatically select an appropriate objective function from historical data are necessary. This paper presents two methods to estimate weighting factors of the objective function in the scheduling problem from historical data, given the information of operation time and setup costs. We propose a machine learning-based method, and an inverse optimization-based method using the input/output data of the scheduling problems when the weighting factors of the objective function are unknown. These two methods are applied to a multi-objective parallel machine scheduling problem and a real-world chemical batch plant scheduling problem. The results of the estimation accuracy evaluation show that the proposed methods for estimating the weighting factors of the objective function are effective.

Environmental life-cycle assessment of concrete produced in the United States

Concrete is a primary material in infrastructure projects and is a significant contributor to global climate emissions. However, there is a lack of readily available cement and ready-mix concrete inventory data for evaluating the environmental performance of the industries. This study describes the development of cradle-to-gate inventories for U.S. ready-mix concrete and gate-to-gate inventories for portland cement production technologies. These life-cycle inventories provide baselines for the environmental releases associated with concrete that is used for major infrastructure projects. The inventories are incorporated into the publicly available Greenhouse gases, Regulated Emissions, and Energy use in Technologies (GREET) model. The life-cycle inventories are created using facility-level environmental release data from U.S. datasets normalized to activity levels which are based on production capacity and utilization data provided by Portland Cement Association (PCA) and the U.S. Geological Survey Minerals Yearbook. Unit processes for limestone quarrying, sand and gravel quarrying, and wet-mix concrete batch plants are developed on the basis of national total point-source environmental releases and production statistics, coupled with corresponding flows associated with off-road fuel consumption and other non-point-source emissions. Midpoint impact assessment results are normalized to provide insight into their relative significance in the context of U.S. total impacts. These findings show that advanced calcination technologies help reduce greenhouse gas emissions, but the full set of releases also highlights the significance of metal releases and particulate-matter emissions generated by non-combustion-related activity.

Un-Interrupted Dispatching Schedule of RMC

One of the key responsibilities of the RMC batch plant manager is to provide Ready Mixed Concrete (RMC) to construction sites. The manager of the RMC batch plant must create a productive schedule for deploying RMC trucks in order to maximize operations both at the batch plant and on construction sites. The current dispatching schedule is primarily influenced by the preferences of the site and the dispatching manager's past experiences. Check to see if the RMC plant manager sends more and more RMC trucks to the necessary construction site. This may cause a line­up of RMC trucks at one site and keep other sites waiting for RMC truck arrivals, which is referred to as a "interruption." In this research, we try to use evolutionary genetic algorithm optimization to decrease the interruption time. Key words: GA’s applications, Un­interrupted Dispatching, dispatching time of RMC trucks, Optimization of Dispatching schedule.

Managing full-scale dry anaerobic digestion: Semi-continuous and batch operation

Dry anaerobic digestion usually results in inhibitors accumulation, which can be solved by adapting operation. Multiple strategies targeting increased stability are implemented in full-scale, but impacts are poorly documented. Two full-scale dry AD plants treating organic fraction of municipal solid waste (OFMSW) were investigated: a semi-continuous plant with compost addition and a batch plant testing different percolate recirculation strategies and inoculum to substrate ratios. Regression tree analysis was used to evaluate the effect of these strategies on methane yields and inhibitors accumulation. Compost addition in the semi-continuous plant reduced volatile fatty acids content but dropped methane flow up to 10 % when compost constituted over 10.1 % in weight of the incoming feedstock. This reduction was linked to the limited availability of easily degradable material in the compost. In batch dry AD, methane yields increased as percolate recirculated raised up to a range of 182–197 m3 (0.342–0.363 m3/t of biomass mix). Recirculation of higher percolate volumes reduced methane production, probably linked to pile compaction and inhibitors accumulation. The ratio of OFMSW, digestate and woodchip (bulking agent) fed was determinant, and methane production was higher when digestate was over 43.1 %, waste between 45 % and 47.5 % and woodchip over 8.2 % in weight in as received basis. Woodchip influenced percolation through the pile and supported reduced total ammonia levels of 3.2 g/l when kept over 8.2 %, which raised to 5.2 g/l for lower values.

Study of the Crystallisation Reaction Behaviour to Obtain Struvite

The potential of N and P recovering from digestate by means of its precipitation in the form of struvite is evident. However, it is necessary to optimise the process at a larger scale, to achieve results that can be extrapolated to evaluate the technical and economic feasibility of the process at an industrial scale. In this work, batch and pilot plant tests were carried out in order to consolidate, at a sufficiently relevant scale, the results obtained at lab scale. For this purpose, the parameters that have the greatest effect on the reaction yield in a fludised bed reactor were selected (Mg and P concentration, flow rate of the fluidising agent (air) and reaction time). Digestate produced in anaerobic digestion plant from pig manure was used as raw material. According to the results obtained, for the struvite crystallisation reaction, the great operational levels for the Mg/P, N/P, air flow rate and reaction time are 1.5, 4.0, 6.0 NL·min−1 and 0.5 h, respectively. Finally, a study was carried out to establish the agronomic potential of the salt (struvite) as a biofertiliser in the turf crop, obtaining a similar behaviour of the struvite used in this work to that of commercial struvite.Graphical

Integration of Scheduling and Control for Chemical Batch Plants under Stochastic Uncertainty: A Back-Off Approach

Integration of Scheduling and Control for Chemical Batch Plants under Stochastic Uncertainty: A Back-Off Approach

SHOTCRETE DELIVERY PROCESS AT DEEP MILL LEVEL ZONE UNDERGROUND MINING PT. FREEPORT INDONESIA

In-ground support of underground mine, it is important to understand the shotcrete delivery process, since shotcrete is the main buffer applied. The problem found in this study is that there is an indication that the shotcrete volume has not matched the reported shotcrete. So it is important to acknowledge the shotcrete delivery process, the difference in volume, and the correction factor. Variables used to calculate shotcrete volume and correction factors obtained in nine main variables as follow: shotcrete trace length, shotcrete trace width, shotcrete trace height, shotcrete transfer distance, shotcrete transfer time, shotcrete transfer time, material weight, material density and shotcrete volume in the Daily Report. The descriptive statistical method is used to select anomaly data outside the population. The research results that the average volume of shotcrete on the Batch Plant is 4.83 m³, then transferred to WS Mixer reduced to 4.76 m³, and at Getman, the Mixer was reduced by 3.92 m³. The volume difference is due to the unequal aggregate conditions of the shotcrete constituents, the Mixer drum rotates outside the specified speed, and shotcrete hardening occurs on the drum causing a reduction in drum volume capacity. Based on these differences, conversion values were obtained to the reported shotcrete volume, which is 0.87 with the shotcrete assumption reported at 4 m³, 0.89 assuming 5 m³, and 0.79 assuming 4.98 m³.

MILP performance improvement strategies for short-term batch production scheduling: a chemical industry use case

This paper presents the development and mathematical implementation of a production scheduling model utilizing mixed-integer linear programming (MILP). A simplified model of a real-world multi-product batch plant constitutes the basis. The paper shows practical extensions to the model, resulting in a digital twin of the plant. Apart from sequential arrangement, the final model contains maintenance periods, campaign planning and storage constraints to a limited extend. To tackle weak computational performance and missing model features, a condensed mathematical formulation is introduced at first. After stating that these measures do not suffice for applicability in a restrained time period, a novel solution strategy is proposed. The overall non-iterative algorithm comprises a multi-step decomposition approach, which starts with a reduced scope and incrementally complements the schedule in multiple subproblem stages. Each of those optimizations holds less decision variables and makes use of warmstart information obtained from the predecessor model. That way, a first feasible solution accelerates the subsequent improvement process. Furthermore, the optimization focus can be shifted beneficially leveraging the Gurobi solver parameters. Findings suggest that correlation may exist between certain characteristics of the scheduling scope and ideal parameter settings, which yield potential for further investigation. Another promising area for future research addresses the concurrent multi-processing of independent MILPs on a single machine. First observations indicate that significant performance gains can be achieved in some cases, though sound dependencies were not discovered yet.

Economic and environmental impacts of utilizing lower production temperatures for different bitumen samples in a batch plant

The utilization of hot mix asphalt (HMA) for road construction necessitates high temperatures during mixing bitumen and aggregate at asphalt plant. The required (mixing) production temperature is calculated by the standard method (ASTM 2493). The application of this method for polymer modified bitumen (PMB) and warm mix asphalt (WMA) have tendency of higher temperatures. Therefore, some alternative methods suggested by literatures for the determination of production temperature for PMB and WMA have been implemented aiming to determine lower temperatures than the standard method (ASTM 2493). Moreover, the economic impacts of the determined production temperatures through different models are evaluated by the estimation of energy consumption in terms of electricity and natural gas costs for the batch type asphalt plants. Besides, the possible environmental effects are calculated by considering the carbon dioxide emissions. The results of this study have shown that the reduction in production temperatures led to a significant decrease in the total construction cost of each type of asphalt and a significant reduction in the estimated carbon dioxide emission. The results of this study can be used as a reference point for the estimation of both economic and environmental impacts of utilizing lower production temperatures for different bitumen samples.

Laboratory and field investigation of the effect of polymerized pellets on the fatigue and low-temperature performance of asphalt mixtures

There are some difficulties attributed to the use of polymer modified binder (PMB) such as low storage stability, low workability, etc. Polymerized pellets (PP), which are added directly to the asphalt mixtures in the mixer of batch plants, are introduced as an alternative to PMB. In this paper, the viscoelastic, fracture, fatigue and low-temperature behavior of the PP modified mixtures were investigated using the direct tension cyclic fatigue, Semi-Circular Beam (SCB) fracture, dynamic modulus and the uniaxial thermal stress and strain tests (UTSST) on both aged and unaged specimens. The results were compared to mixtures containing PMB and control neat hot mix asphalt (HMA). The results indicated that using both polymers led to an improvement in the performance properties of asphalt mixtures, and aging had a substantial adverse impact on the fatigue and low-temperature cracking resistance of asphalt mixtures especially when modified with PMB or PP. The fatigue, low-temperature and fracture performance of the PP modified mixtures were either better than or equal to the mixtures containing PMB. However, the high aging susceptibility of the PP modified mixtures raises questions for further research.

Cyclic scheduling and heat integration of batch process: Design of heat storage vessels

Scheduling over longer time horizons has limitations such as large problem size and computational complexity. Therefore, there is universal concern to establish novel modeling approaches to efficiently manage various complex characteristics. In this work, a rigorous Unit Specific Event Based model (USEB) is proposed for long-term scheduling of batch plants. The proposed approach addresses the complete scheduling of long-term operational horizon by considering start-up, cycle and finishing periods. The start-up period takes care of intermediate material states requirement at the beginning of first cycle and finishing period effectively utilizes the leftover intermediate states in final cycle. Using active task concept, the cyclic scheduling, direct and indirect heat integration possibilities are accurately modelled by considering design of heat storage vessels. This comprehensive computational approach highlights the importance of judicious use of direct and indirect heat integration in process industries and cyclic scheduling for complex and long term scheduling problems.

Novel hybrid discrete differential evolution algorithm for the multi-stage multi-purpose batch plant scheduling problem

The multi-stage multi-product batch plant scheduling problem is an important part of batch chemical industry scheduling problems. Different from the multi-purpose batch scheduling problem, this problem can be characterized by multiple stages with non-identical parallel units, and multiple batches of customer orders. Numerous methodologies for this problem have been investigated to addressing scheduling cases of different production systems in the past two decades. This paper focus on the large-scale batch plant scheduling problem by minimizing the make-span in the scheduling horizon. And a novel hybrid discrete differential evolutionary algorithm is proposed to handle this problem. First, a novel two-line encoding scheme is constructed based on discrete and continuous variables. The sequence of orders is represented by a time-based representation method. Second, two novel mutation methods are proposed within the framework of encoding method. Two methods provide multiple search directions which helps improve the exploration ability and diversity of the population. At last, two local permutation methods are applied to improve the local optimal for the algorithm. The proposed work is tested through several real industrial instances with different sizes and characteristics by comparing with mixed integer linear programming method and meta-heuristic algorithms. The improvement of proposed work is also analyzed by the instances. The results report the efficiency and effectiveness of the novel proposed evolutionary algorithm in solving large scale multi-stage multi-product batch plant scheduling problem.

Efficient Process for Batch Analysis

AbstractIdentification of quality issues in batch production is relevant for stable and successful production. The identification of systematic quality issues and the identification of root causes is complex and challenging. Powerful multivariate statistical methods exist to perform batch analysis, yet their application is difficult due to data diversity and complexity in model training. Here, insights from data analysis projects on four batch plants are presented and real world challenges are reviewed. This leads to a semi‐automatic process for more efficient batch analysis overcoming the mentioned tedious tasks.

Synthesis of an Optimal Schedule and Water Network for a Multipurpose Multiproduct Textile Industry through a Sequential MILP-NLP Technique

Textile factories are usually batch plants. Their high freshwater consumption and large emissions of toxic wastewater present a threat to water availability and public health. Wastewater minimizati...

Utilization of Self-Consolidating Concrete Technology for Large Placements in New Nuclear Construction: A Study on Self-Consolidating Concrete Stability and Constructability

Abstract This paper discusses the use of self-consolidating concrete (SCC), in a large-scale placement during new nuclear plant construction. SCC has provided cost and labor savings and construction process improvements. Use of SCC has been effective in schedule acceleration by reducing placement durations and early curing termination with rapid strength gain. Self-consolidating properties were particularly of value at placements of poor access as internal vibrators were not effective and large surface area made form vibrators ineffective beyond the perimeter. This paper provides a discussion of technical challenges encountered in development of a properly functioning SCC mixture, and testing performed to overcome these challenges. The paper discusses the engineering considerations and laboratory and field trials preparing for a 3950 m3 placement for the base of a nuclear plant. The placement was anticipated to take 35 h at 110 m3/h batch plant output. Overview of mix design specifications and mix development is provided. The mixture satisfied the laboratory test parameters including SCC tests; stability and column segregation. However, the mixture had to undergo rounds of constructability testing beyond laboratory development prior to the large placement. Among the constructability properties was the distance SCC will move with the stone before stopping. A long trough was filled as a mockup for and subsequently cored. The number of pumps and pump nozzle placement spacing was determined as a result. Slump flow retention as the polycarboxylate loses effectiveness and foaming of the mixture when pumped were also investigated. Foaming was attributed to viscosity, air entrainer, and pump pressures or rate of pumping. Laboratory trials with various admixtures and pumping studies in the field were conducted to achieve a mixture that retains slump flow and can be pumped at the desired rates without instability or excessive foaming. The placement was completed successfully in 35 h over multiple shifts.

Challenges in Optimization and Control of Biobased Process Systems: An Industrial-Academic Perspective

Biobased slurry processes and large-scale continuous chemical production share many similarities. These concern not only process technologies and equipment but also staffing (positions/educations) and plant operations, including methods and technologies for planning, scheduling, and control. Process Systems Engineering (PSE) approaches have been applied successfully in large-scale chemical production to optimize processes of high complexity. By replacing rules-of-thumb and oversimplified calculations with rigorous quantification and automation, PSE increases the profitability of a wide range of processes. Such improvement potential also exists in the bioindustry, which traditionally comprises many small-scale and batch plants deployed in food or pharmaceutical production. The article elucidates the most important operational challenges of such processes. It brings together academic research and industrial perspectives by contrasting a careful revision of literature with a survey of personnel working in industrial biobased production settings in Denmark. This analysis allows showing the existence of a gap between academic research focus and industrial need, for instance, concerning production processes with humans-in-the-loop. Furthermore, differences between chemical and biobased processes are pointed out, highlighting some limitations of applying PSE to biobased processes. A deeper understanding of these limitations is fundamental to assessing risks when identifying process improvement potential. Finally, the article suggests a possible roadmap for the development of frameworks for the optimization of industrial biobased processes based on existing works in chemical processing.