Batch Plants Research Articles

An end-to-end scheduling digital twin for multistage batch plants considering safety

Modern multistage batch plants, characterized by products processed through a series of stages with competing processing units, face challenges in optimizing scheduling and considering inserted events such as breakdowns and maintenance operations simultaneously in current highly dynamic and volatile market. It necessitates robust scheduling tools for efficiency, flexibility, and safety. Previous mathematical programming and meta-heuristic methods, while effective, are time-consuming for large or complex problems. Hybrid models and machine learning approaches have shown promise but often lack robustness against real-world disruptions. In response to these challenges, this work proposes an end-to-end digital twin (DT) solution based on a deep reinforcement learning model for multiproduct multistage batch plants. This model provides a versatile solution for scheduling processes of varying sizes without the need for extensive retraining, seamlessly incorporating dynamic factors. Tests on the model demonstrate its rapid interaction and robust solution capabilities, highlighting its comprehensive advantages in processing speed and result quality.

Metode Pelaksanaan Pembuatan PC Spun Pile dengan Mutu F'c 52 MPa Pada PT. Wijaya Karya Beton Tbk PPB Majalengka

A concrete plant, also known as a batch plant or batching plant or a concrete batching plant, is equipment that combines various ingredients to form concrete. This implementation aims to determine the manufacturing process and quality control of concrete product production. The method used in this implementation is descriptive quantitative. Steps include operational variables to determine the problem and indicators of the cause of the problem. Data collection through observation and documentation. The results of this implementation show that the implementation of manufacturing at PT WIKA Beton Tbk PPB Majalengka aims to make good concrete products, the factors for making PC Spun Pile products are made based on work instructions or standard operational procedures.

Virtual Modelling of the Stress-Strain State of the Bar Thrust System in the Chrome Plating Technological Process

Abstract Chrome plating of long bars in conventional batch plants proves to be challenging, requiring complex lifting equipment and considerable vertical space. Therefore, the use of continuous chrome plating plants, which allow controlled passage of the bars through the chrome plating bath, is preferred. The continuous hard chrome plating plant operates automatically, controlling the entire process from the loading to the discharge of chrome plated bars. The feed and discharge mechanisms must allow the flow of electric current through the bars subject to the chrome plating. This calls for the design of some mechanisms capable of ensuring a constant feed of bars and their efficient discharge. This paper presents a virtual study using finite element analysis (FEA) focused on the strength assessment of the mechanism including the drive pin, the levers and the sliding plates responsible for the movement of the bars. As a result of the study, essential data on critical stresses, local deformations and factor of safety (FOS) for the whole mechanism were obtained, thus providing valuable information for the optimization of the chrome plating process.

Cooperative game theory approach towards capital equipment procurement in construction projects: a concrete batch plant case study

This study explores how contractors can benefit from joint resource procurement in construction projects, particularly in case with capital-intensive ones. Capital equipment is costly and has a long beneficial life compared to other resources; hence, minimizing its long-term costs is pivotal. Also, the lack of open dialogue under a legal structure in projects may deprive project participants from many beneficial partnering opportunities. Given this background, a comprehensive framework based on cooperative game theory is introduced to form a systematic partnership among contracting parties to share their capital equipment. Cooperative game theory is suggested as the basis for increase the gains to all parties and fair and efficient allocation of the incremental benefits of cooperation. The proposed flowchart is then applied to a real-world project and various coalitional cooperative game theory solutions for dividing gains equitably, from sharing a site-located concrete batch plant among three contractors are evaluated and the most stable cost allocation is identified. Results reveal that with the application of this cooperative procurement, the three contractors can achieve an average of twenty three percent considerable savings through practical alliance that deliver vast improvements in project productivity and performance.

Carbon sequestration in cementitious systems through CO2-rich hydration and chemically enforced CO2 mineralization

Cementitious materials as a medium of carbon capture, and utilization (CCU) have recently attracted considerable attentions. Atmospheric CO2 can be absorbed in hardened concrete, which can be also accelerated by early age CO2 curing. Compared to the CO2 curing of concrete materials, in-situ CO2 mixing technology can be widely applied because it can be used in a batch plant without an additional curing facility. In this study, the CO2 mixing time was set as the primary variable to elucidate the precipitation of the carbonate phases in the early stages and its effect on cement hydration. The dissociated CO2 is directly mineralized into calcium carbonate (CaCO3) in calcite phase. In addition, the longer the CO2 mixing time, the greater the precipitation of calcite (i.e., CCU capacity), thereby densifying the internal microstructure and improving early strength development. Interestingly, a certain amount of calcite converted to monocarboaluminate—an important factor for quantitatively assessing the degree of mineral carbonation in cementitious materials.

A recurrent reinforcement learning strategy for optimal scheduling of partially observable job-shop and flow-shop batch chemical plants under uncertainty

This study presents a methodology that makes use of Deep Recurrent Q-Learning to develop an agent that acts as an online scheduler for flow-shop or job-shop batch plants with zero-wait restriction under uncertainty. The environment is assumed to be partially observable, i.e., it does not follow the Markov property and information has to be gathered from previous time intervals. The processing times of the machines are unknown to the agent whereas production demand realizations are provided during the operation and not known a priori. The agent aims to complete the demands and to minimize the makespan of the process. Moreover, the agent should avoid violation of constraints associated with product allocation, time horizon, and storage capacity. Three case studies featuring two job-shops and one flow-shop are presented to show the benefits of this framework with environments where the information is limited. Results showed that the agents can generate schedules considering the uncertain parameters of the system while aiming to reduce the makespan of the process. Tests on the agents resulted in small errors in the decision-making process (less than 2%) thus demonstrating that DRQN can serve as a reliable tool for online scheduling subject to uncertainty in partially observable job and flow-shops.

Optimization of heat management and utilization in batch processes

Batch plants boast operational flexibility in manufacturing process and resource sharing, making batch processes suitable to fluctuating-demand or small-scale production. However, given time dependence of operational behavior, batch processes are more demanding in energy integration than continuous processes, a task which is especially important amid the global net zero-emission current. It is, therefore, imperative to develop sustainable design strategies for intermittent processes. The study introduced a targeting method and developed an innovative over-time heat integration technique to optimize energy utilization in batch processes, involving use of heat transfer media for heat recovery over various time intervals and a heat makeup strategy to heat the media over different periods, in order to enhance heat-energy utilization efficiency. The technique is applicable to both fixed and unspecified batch schedules, the latter of which involves simultaneous design of production schedule and heat recovery systems, increasing flexibility and improving heat energy utilization. Case studies are conducted to demonstrate the proposed method. With the heat makeup technique, energy efficiency can be significantly enhanced, achieving energy savings of 51% and 93.1% in hot and cold utility, respectively. Meanwhile, the simultaneous design of production schedule and heat integration can boost savings up to 53% and 99%. Results have proven the benefits of the technique, including significant reduction of energy consumption and carbon emission, contributing to clean production, environmental protection, and sustainable development.

Multicut logic‐based Benders decomposition for discrete‐time scheduling and dynamic optimization of network batch plants

AbstractThis study presents the first application of a logic‐based Benders decomposition (LBBD) technique in the field of simultaneous scheduling and dynamic optimization (SSDO), applied to network batch processes with a discrete‐time scheduling formulation. The proposed algorithm employs neighborhood information of ordered discrete decisions (e.g., batching variables) to generate cuts, rather than relying on traditional cut generation techniques based on dual information that are implemented in generalized Benders decomposition (GBD) algorithms. The proposed algorithm relies on solving multiple subproblems per iteration, which is a feature that allows the generation of multiple cuts per iteration thus producing accurate approximations of the objective function in shorter computational times. This results in the herein proposed multicut logic‐based discrete Benders decomposition (MLD‐BD) algorithm, which enables features such as a pruning strategy, and a cut‐off technique. Two case studies are used to demonstrate the computational advantages of the MLD‐BD framework against GBD and heuristic methodologies.

Fatigue Performance Evaluation of Fiber-Reinforced Asphalt Mixtures with Four-Point Bending Beam and Uniaxial Fatigue Tests

This study aimed to evaluate the fatigue cracking potential of aramid fiber–reinforced asphalt mixtures. Three fiber-reinforced asphalt mixtures (FRAMs) were prepared employing a singular aggregate type (diabase stone), one binder type (PG76-22), and two distinct combinations of aramid fibers at varying dosages. Two types of fiber were used at different dosages by weight of mix: polyolefin/aramid (PFA) fibers at a dosage 0.05% and sasobit-coated aramid (SCA) fibers at dosages of 0.01% and 0.02%. Additionally, an unreinforced (control) mix was also produced for comparison purposes. Each of the mixtures was produced at a batch plant, adhering to the fiber manufacturer’s recommended mixing methods, while maintaining a constant binder content of 5.5% based on the total mix weight. Four-point beam (4PB) fatigue, uniaxial fatigue (UF), and Texas overlay tests (OT) were performed to characterize the fatigue and reflective cracking behavior of FRAM. The 4PB test results suggest that FRAM improved the fatigue life at lower strain levels (400 µε and 600 µε); however, at higher microstrain (800 µε) level, FRAM exhibited lower fatigue life than the control mix. Damage characteristic curves obtained from the UF tests showed better fatigue tolerance for reinforced mixtures, regardless of microstrain level. For cycles to failure under tensile loading, UF and OT test results indicated lower fatigue life of reinforced mixtures compared with the control mix. Therefore, under flexural loading the fiber-reinforced asphalt mixtures tend to improve the fatigue life. However, for direct tension load, reinforced mixtures tend to deteriorate the fatigue life when comparing cycles to failure at constant strain amplitude.

Integration of Planning and Scheduling for Large-Scale Multijob Multitasking Batch Plants

Integration of Planning and Scheduling for Large-Scale Multijob Multitasking Batch Plants

A new approach for solving global optimization and engineering problems based on modified sea horse optimizer

Abstract Sea horse optimizer (SHO) is a noteworthy metaheuristic algorithm that emulates various intelligent behaviors exhibited by sea horses, encompassing feeding patterns, male reproductive strategies, and intricate movement patterns. To mimic the nuanced locomotion of sea horses, SHO integrates the logarithmic helical equation and Levy flight, effectively incorporating both random movements with substantial step sizes and refined local exploitation. Additionally, the utilization of Brownian motion facilitates a more comprehensive exploration of the search space. This study introduces a robust and high-performance variant of the SHO algorithm named modified sea horse optimizer (mSHO). The enhancement primarily focuses on bolstering SHO’s exploitation capabilities by replacing its original method with an innovative local search strategy encompassing three distinct steps: a neighborhood-based local search, a global non-neighbor-based search, and a method involving circumnavigation of the existing search region. These techniques improve mSHO algorithm’s search capabilities, allowing it to navigate the search space and converge toward optimal solutions efficiently. To evaluate the efficacy of the mSHO algorithm, comprehensive assessments are conducted across both the CEC2020 benchmark functions and nine distinct engineering problems. A meticulous comparison is drawn against nine metaheuristic algorithms to validate the achieved outcomes. Statistical tests, including Wilcoxon’s rank-sum and Friedman’s tests, are aptly applied to discern noteworthy differences among the compared algorithms. Empirical findings consistently underscore the exceptional performance of mSHO across diverse benchmark functions, reinforcing its prowess in solving complex optimization problems. Furthermore, the robustness of mSHO endures even as the dimensions of optimization challenges expand, signifying its unwavering efficacy in navigating complex search spaces. The comprehensive results distinctly establish the supremacy and efficiency of the mSHO method as an exemplary tool for tackling an array of optimization quandaries. The results show that the proposed mSHO algorithm has a total rank of 1 for CEC2020 test functions. In contrast, the mSHO achieved the best value for the engineering problems, recording a value of 0.012 665, 2993.634, 0.01 266, 1.724 967, 263.8915, 0.032 255, 58 507.14, 1.339 956, and 0.23 524 for the pressure vessel design, speed reducer design, tension/compression spring, welded beam design, three-bar truss engineering design, industrial refrigeration system, multi-product batch plant, cantilever beam problem, and multiple disc clutch brake problems, respectively. Source codes of mSHO are publicly available at https://www.mathworks.com/matlabcentral/fileexchange/135882-improved-sea-horse-algorithm.

Evaluation of Low- and Intermediate-Temperature Cracking Performance of Fiber-Reinforced Asphalt Mixtures

This study was conducted to assess the impact of aramid fiber reinforcement on the fatigue and thermal cracking performance of asphalt mixtures at intermediate and low temperatures, respectively. The study also involved evaluating the experimental consistency and ranking fiber-reinforced asphalt mixtures (FRAMs) based on their performance. One aggregate (diabase stone), one binder type (PG 76-22), and two combinations of aramid fibers, added at different dosages, were used to produce three FRAMs. These fibers included polyolefin/aramid (PFA) fibers at 0.05% dosage by mix weight and Sasobit-coated aramid (SCA) fibers at 0.01% and 0.02% dosages. An unreinforced (control) mix was also produced without any reinforcement. All mixtures were produced at a batch plant by keeping their binder content constant (at 5.5%). The manufacturer recommended dry and wet mixing times were used for better fiber distribution and represent actual mixing conditions. The laboratory performance of plant-produced FRAMs was assessed to characterize the low- and intermediate-temperature cracking performance. The indirect tension asphalt cracking test, semi-circular bend test, three-point bending beam tests, and uniaxial fatigue tests were performed at intermediate temperature; the disk shape compact tension test and thermal stress restrained specimen test tests were performed at low temperature. Fibers were successful at improving the fatigue cracking of asphalt mixtures based on intermediate-temperature testing results. In addition, fiber reinforcement, regardless of dosage and combination, showed improvements in low-temperature (thermal) cracking performance. Furthermore, based on ranking analysis for selected performance indicators from each experiment, the SCA 0.02% reinforced mix on average showed the highest cracking resistance.

Integration of planning, scheduling, and control of no-wait batch plant

The batch process plays an important role in industrial production. Among them, production processes that have specific requirements for operational continuity in each processing stage should consider the no-wait constraint to meet the production reality. The batch process with a no-wait constraint is a typical NP-hard problem. In this work, we propose a framework for the integration of planning, scheduling, and control. We also propose a decomposition method with an improved genetic algorithm to solve the integration problem of scheduling and control for the no-wait batch process. The integrated formulation represents a typical mixed-logic dynamic optimization (MLDO) problem, which involves logical disjunctions and operational dynamics. Then, we address the integrated problem as a grey-box optimization problem, using data-driven feasibility analysis and surrogate models to approximate the unknown black-box constraints. Finally, we test specific production instances to demonstrate the feasibility and superiority of the proposed integration model of the no-wait batch process and optimization algorithm.

A modified smell agent optimization for global optimization and industrial engineering design problems

Abstract This paper introduces an Improved Smell Agent Optimization Algorithm (mSAO), a new and enhanced metaheuristic designed to tackle complex engineering optimization issues by overcoming the shortcomings of the recently introduced Smell Agent Optimization Algorithm. The proposed mSAO incorporates the jellyfish swarm active–passive mechanism and novel random operator in the elementary SAO. The objective of modification is to improve the global convergence speed, exploration–exploitation behaviour, and performance of SAO, as well as provide a problem-free method of global optimization. For numerical validation, the mSAO is examined using 29 IEEE benchmarks with varying degrees of dimensionality, and the findings are contrasted with those of its basic version and numerous renowned recently developed metaheuristics. To measure the viability of the mSAO algorithm for real-world applications, the algorithm was employed to solve to resolve eight challenges drawn from real-world scenarios including cantilever beam design, multi-product batch plant, industrial refrigeration system, pressure vessel design, speed reducer design, tension/compression spring, and three-bar truss problem. The computational analysis demonstrates the robustness of mSAO relatively in finding optimal solutions for mechanical, civil, and industrial design problems. Experimental results show that the suggested modifications lead to an improvement in solution quality by 10–20% of basic SAO while solving constraint benchmarks and engineering problems. Additionally, it contributes to avoiding local optimal stuck, and premature convergence limitations of SAO and simultaneously.

Polluting under the Radar: Emissions, Inequality, and Concrete Batch Plants in Houston.

Small industrial sources collectively release large amounts of pollution, including particulate matter (PM) that contributes to air quality problems in the United States and elsewhere. We study one such type of industrial facility, concrete batch plants, and analyze PM emissions and siting patterns of 131 plants located in Harris County, Texas. We find that concrete batch plants in Harris County are collectively a major pollution source, contributing between 38 and 111 tons of primary PM2.5 emissions (between 26%-76% of PM2.5 from the median Texas oil refinery) and between 109 and 493 tons of primary PM10 emissions (between 64%-290% of PM10 from the median refinery). Estimates from an integrated assessment model suggest that health damages from the PM2.5 emissions alone amount to $29 million annually, reflecting two additional premature deaths per year. We further find that concrete batch plants in Harris County are disproportionately located in census tracts with more low-income, Hispanic, and Black populations, thereby raising important environmental justice questions. On the basis of these findings, we argue that small pollution sources require more air quality monitoring and emissions reporting and that regulatory agencies should consider cumulative environmental and health impacts of these sources as part of the permitting process.

A time-bucket MILP formulation for optimal lot-sizing and scheduling of real-world chemical batch plants

We propose a new time-bucket MILP model for lot-sizing and scheduling problems arising in chemical batch plants. The main idea behind time-bucket models is to partition time into fixed-length macroperiods and flexible length microperiods, that lie within the macroperiods. We show that the time-bucket model benefits from advantages of both continuous and discrete time representations. It allows to include important real-world constraints, can be solved with moderate computational effort, and thus promotes MILP for large-scale, industrial problems. We investigate the scalability of the model and apply it to a formulation and filling process from an industrial agrochemical production with 7 formulation lines, intermediate buffer tanks, and 7 filling lines. We optimize a one month period with 50 intermediates, and 83 finished products. A comparison of the MILP solution to a discrete event simulation solution shows that 17% of production capacity can be freed up and significant improvement in on-time delivery.

Novel memetic of beluga whale optimization with self-adaptive exploration–exploitation balance for global optimization and engineering problems

A population-based optimizer called beluga whale optimization (BWO) depicts behavioral patterns of water aerobics, foraging, and diving whales. BWO runs effectively, nevertheless it retains numerous of deficiencies that has to be strengthened. Premature convergence and a disparity between exploitation and exploration are some of these challenges. Furthermore, the absence of a transfer parameter in the typical BWO when moving from the exploration phase to the exploitation phase has a direct impact on the algorithm’s performance. This work proposes a novel modified BWO (mBWO) optimizer that incorporates an elite evolution strategy, a randomization control factor, and a transition factor between exploitation and exploitation. The elite strategy preserves the top candidates for the subsequent generation so it helps generate effective solutions with meaningful differences between them to prevent settling into local maxima. The elite random mutation improves the search strategy and offers a more crucial exploration ability that prevents stagnation in the local optimum. The mBWO incorporates a controlling factor to direct the algorithm away from the local optima region during the randomization phase of the BWO. Gaussian local mutation (GM) acts on the initial position vector to produce a new location. Because of this, the majority of altered operators are scattered close to the original position, which is comparable to carrying out a local search in a small region. The original method can now depart the local optimal zone because to this modification, which also increases the optimizer’s optimization precision control randomization traverses the search space using random placements, which can lead to stagnation in the local optimal zone. Transition factor (TF) phase are used to make the transitions of the agents from exploration to exploitation gradually concerning the amount of time required. The mBWO undergoes comparison to the original BWO and 10 additional optimizers using 29 CEC2017 functions. Eight engineering problems are addressed by mBWO, involving the design of welded beams, three-bar trusses, tension/compression springs, speed reducers, the best design of industrial refrigeration systems, pressure vessel design challenges, cantilever beam designs, and multi-product batch plants. In both constrained and unconstrained settings, the results of mBWO preformed superior to those of other methods.

Optimization of production schedules of multi-plants for dispatching ready-mix concrete trucks by integrating grey wolf optimizer and dragonfly algorithm

PurposeThe paper aims to provide an efficient dispatching schedule for ready-mix concrete (RMC) trucks and create a balance between batch plants and construction sites.Design/methodology/approachThe paper focused on developing a new metaheuristic swarm intelligence algorithm using Java code. The paper used statistical criterion: mean, standard deviation, running time to verify the effectiveness of the proposed optimization method and compared its derivatives with other algorithms, such as genetic algorithm (GA), Tabu search (TS), bee colony optimization (BCO), ant lion optimizer (ALO), grey wolf optimizer (GWO), dragonfly algorithm (DA) and particle swarm optimization (PSO).FindingsThe paper proved that integrating GWO and DA yields better results than independent algorithms and some selected algorithms in the literature. It also suggests that multi-independent batch plants could effectively cooperate in a system to deliver RMC to various construction sites.Originality/valueThe paper provides a compelling new hybrid swarm intelligence algorithm and a model allowing multi-independent batch plants to work in a system to deliver RMC. It fulfills an identified need to study how batch plant managers can expand their dispatching network, increase their competitiveness and improve their supply chain operations.

New electrochemical reactor design for emergent pollutants removal by electrochemical oxidation

This paper presents the theoretical and experimental confirmation of the performance of a novel pre-pilot reactor design implementing a boron-doped diamond (BDD) anode to destroy emerging pollutants by electrochemical oxidation. Turbulent flow simulation and secondary current distribution modeling with a COMSOL Multiphysics software were used to assess the engineering capabilities of the reactor along with the oxidant BDD(·OH) electrogeneration at the anode. The antibiotic ciprofloxacin (CIP) was chosen as model molecule to assess the oxidation power achieved with the pre-pilot batch plant. In sulfate medium where BDD(·OH) was the main oxidant, faster degradation was determined by increasing current density, CIP content, and pH. The effect of pH was explained by the transformation of the cationic form of CIP in acidic medium into its more easily oxidizable anionic form in alkaline medium. In chloride medium, CIP was more rapidly removed by the faster attack of the generated active chlorine. The degradation was decelerated in carbonate medium by its scavenging effect and in the presence of humic acid by its competitive oxidation with BDD(·OH). The antibiotic abatement also dropped down in tap water and synthetic urine. An almost total mineralization was achieved with a constant energy cost per unit COD mass of 0.6 ± 0.1 kWh (g COD)−1. The initial N of CIP was pre-eminently converted into nitrate, alongside nitrite and ammonia to lesser extent. Recalcitrant acetic, oxalic, and formic acids were detected as final carboxylic acids.

Simultaneous Scheduling and Synthesis of Industrial Water Allocation Networks

This work addresses integration of batch scheduling with water allocation, recycle and reuse opportunities for freshwater minimization in batch plants via sequential and simultaneous methodologies. The presented scheduling model is based on state task network representation and unit-specific event based continuous time formulation. In the production scheduling model, a three-index finish time variable has been considered for handling multiple states having different processing time durations for the same task in a processing unit. The scheduling model introduces constraints to handle storage violations for production and consumption of the same state in the same unit. In the water network model for freshwater minimization, a regeneration unit along with a central water storage tank has been included to exploit the possibility of water reuse in the washing units. Four case studies are solved with single and multiple contaminants to evaluate the performance of the proposed model, which gives better savings in terms of freshwater consumption and thus also minimizes the effluent generation. Additionally, a preliminary analysis for two-objective optimization is presented where revenue is maximized, and the total water cost is minimized simultaneously using the weighted-sum method.