Batch Plants Research Articles

Comparison of particle swarm optimization and genetic algorithm for multiproduct batch plant design of protein production

Comparison of particle swarm optimization and genetic algorithm for multiproduct batch plant design of protein production

A matheuristic approach for the design of multiproduct batch plants with parallel production lines

Batch processes are typically used to manufacture, among other, specialty and fine chemicals. As the construction of grassroot batch plants requires major investments, models for determining the optimal design of such plants have been developed over the past decades. These models are often formulated as Mixed Integer Linear Programming (MILP) models which are solved exactly. In a previous study, we introduced the concept of parallel production lines as a design option into existing mathematical plant design models. The design problem now also aims at optimising the number of production lines, their design and the allocation of products (and production quantities) to the installed lines. However, with this extension, the complexity increases significantly. To tackle this combinatorial divergence, we formulated a matheuristic solution approach which combines an iterated local search metaheuristic with exact MILP calculations. In this paper, the hybrid solution method is described and its performance, in comparison to an exact algorithm, is illustrated for several example problems. It was found that our matheuristic obtained very good solutions in significant lower computation time. As a consequence, this technique is suitable to solve more realistic instances and enables us to expand these design models with e.g. different objectives in the future.

A novel metaheuristic framework for the scheduling of multipurpose batch plants

A genetic algorithm (GA) is proposed along with a general framework for the scheduling of a typical multipurpose/product batch plant. The majority of literature regarding these problems make use of mathematical programming methods. Modelling problems in this manner leads to numerous binary variables relating to material balance and sequence of batches along long time horizons, thus resulting in large computational time. The proposed GA does not suffer the same scalability issues of mathematical programming approaches. The GA makes use of a coupled chromosome system with specific crossover and mutation functions utilised with the purpose of profit maximisation. Results show that optimal or close-to-optimal solutions can be achieved with a reduction of up to 98.53% computational time in certain cases.

Optimal Synthesis of Batch Water Networks Using Dynamic Programming

Water minimization in the process industry is becoming increasingly important due to increasingly stringent environmental legislation, especially for batch plants. This work proposes a dynamic programming (DP) method for the optimal design of water-using networks in batch plants. DP is a powerful framework for dealing with a large spectrum of multistage decision-making problems and has been applied in numerous chemical engineering problems. The proposed methodology is explained as follows. Firstly, based on the start time and end time of each operation, the whole process is divided into N stages. Secondly, the water requirement of water-using units in each stage is satisfied and the state of stored water and wastewater generation is determined. The backward procedure of DP is used to solve the DP problems. The target of freshwater consumption of the process and the optimal design of the water network are obtained simultaneously. In order to display the versatility of the proposed approach, four examples from literature are considered. Example 1 is a completely batch process with a fixed flowrate problem. Example 2 is a hybrid batch water system comprising various modes of operations and operating patterns. Example 3 is a fixed-mass load problem with a regeneration unit, while example 4 considers the batch water network design with multiple contaminants. The results obtained in this work were comparable with the results from literature, implying that it can be applicable to both mass transfer-based and non-mass transfer-based batch water networks.

Influence of recycled concrete and steel slag aggregates on warm-mix asphalt properties

This paper focuses on the comparison of the mechanical performance of three warm-mix asphalt (WMA) blends with recycled concrete aggregate (RCA) or electric arc furnace slag (EAFS) as substitutes of part of the aggregate. A conventional hot mix asphalt (HMA) and a WMA without by-products were used as references. The evaluation was carried out in a laboratory by means of testing specimens taken from experimental pavement sections built in real production, laying and compaction circumstances. Performance testing included wheel-tracking tests, four-point bending tests, and indirect tensile strength to assess water sensitivity. An organic wax and a chemical surfactant were applied to lower handling temperatures of the WMA under study. Apart from the HMA and the WMA used as references, the study evaluated the influence of introducing 60% of RCA or 30% of EAFS into the WMA blends as substitutes of the aggregate. The obtained results for the WMA with by-products tested allowed to conclude that the introduction of EAFS or RCA into the WMA blends increases Marshall stability and may increase or decrease resistance to rutting. Findings also showed that stiffness modulus is somewhat reduced and fatigue resistance does not change significantly. Additionally, water sensitivity is slightly reduced. Comparing these results with the performance observed elsewhere for WMA without by-products revealed that the addition of RCA or EAFS is satisfactory. The construction of experimental sections used conventional batch plant, paver and compactors without any noticeable technical problems.

Comparing GPU-parallelized metaheuristics to branch-and-bound for batch plants optimization

We systematically compare two approaches with the optimal design of multiproduct batch plants that are widely used, e.g., in the chemical industry. Deterministic algorithms like branch-and-bound achieve global optimality, but often require a prohibitively high computation effort. We propose an alternative, hybrid algorithm by combining two metaheuristics: ant colony optimization (ACO) and simulated annealing (SA), in order to find near-optimal solutions in a reasonable time. We develop a parallel implementation of our hybrid approach on graphics processing units using CUDA. We experimentally compare both approaches, and we show that our hybrid metaheuristic approach $$(\hbox {ACO}+\hbox {SA})$$ yields almost-optimal solutions, while computing them significantly faster than when using branch-and-bound.

Design of a Cost‐Reduced Flexible Plant for Supercritical Fluid‐Assisted Applications

AbstractA novel batch plant for supercritical CO2 applications is proposed which is not equipped with expensive components, such as high‐pressure pumps, making it particularly suitable for bench‐scale use. For the first time, the use of a hanging scale is suggested to weigh the amount of CO2 required for the experiment and the use of the thermodynamics to reach the working conditions. The rig is able to cover different applications, e.g., aerogel drying, impregnation, and extraction, showing high flexibility. An approximate cost analysis has been performed considering as a reference a 150‐mL vessel. It has been calculated that both the setup and running costs are considerably lower than the common batch and semicontinuous rigs.

Big data on a big new market: Insights from Washington State’s legal cannabis market

IntroductionVoters in eight U.S. states have passed initiatives to legalize large-scale commercial production of cannabis for non-medical use. All plan or require some form of “seed-to-sale” tracking systems, which provide a view of cannabis market activity at a heretofore unimagined level of detail. Legal markets also create a range of new matters for policy makers to address. DataPublicly available data were obtained on approximately 45 million individually priced items purchased in the 35 million retail transactions that took place during the first two and a half years of Washington State’s legal cannabis market. Records include product type (flower, extract, lotion, liquid edible, etc.), product name, price, and potency with respect to multiple cannabinoids, notably THC and CBD. Items sold can be traced back up the supply chain through the store to the processor and producer, to the level of identifying the specific production batch and mother plant, the firm that tested the product, and test results. MethodData visualization methods are employed to describe spatial-temporal patterns of multiple correlated attributes (e.g., price and potency) broken down by product. Text-analytic methods are used to subdivide the broad category of “extracts for inhalation” into more homogeneous sub-categories. To understand the competitiveness of the legal cannabis market in Washington we calculate the Herfindahl-Hirschman index (HHI) for processors and retailers. ResultsCannabis prices fell steadily and proportionally at the processor and retailer levels. Retail and wholesale price maintained a roughly 3:1 ratio for multiple product types after some initial fluctuations. Although a wide range of edibles are sold, they account for a modest share of consumer spending; extracts for inhalation are a larger and heterogeneous market segment. The HHI indicates the cannabis market is highly competitive at the processor level, but less so for retail markets at the county level. ConclusionsWashington’s state-legal cannabis market is diverse and rapidly evolving in terms of pricing, products, and organization. Post-legalization, researchers and policy makers may need to think in terms of a family of cannabis products, akin to how we think of new psychoactive substances and amphetamine-type stimulants, not a single drug “cannabis.”

Gate-to-Gate Energy Consumption in Chemical Batch Plants: Statistical Models Based on Reaction Synthesis Type

Energy consumption in the chemical industry is an important operating cost and environmental impact factor and reducing it is also explicitly mentioned as one of the key principles of green chemistry. Energy consumption has thus been included in diverse process design and evaluation tools as a key metric. However, measurements of energy consumption at the process equipment level are scarce, especially in fine chemical production typically performed in multiproduct and multipurpose batch plants. In this work, we present a shortcut approach based on statistical models, such as probability density functions (PDF) and classification trees, for estimating steam consumption which typically represents the highest energy utility consumption in batch plants. The output of these models is in the form of intervals derived from PDF interquartile ranges and as classes derived from the classification trees, respectively. The validation results (i.e., goodness of fit, cross validation, and case studies) show that the mo...

Theoretical and computational comparison of continuous‐time process scheduling models for adjustable robust optimization

Coping with uncertainty in system parameters is a prominent hurdle when scheduling multi‐purpose batch plants. In this context, our previously introduced multi‐stage adjustable robust optimization (ARO) framework has been shown to obtain more profitable solutions, while maintaining the same level of immunity against risk, as compared to traditional robust optimization approaches. This paper investigates the amenability of existing deterministic continuous‐time scheduling models to serve as the basis of this ARO framework. A comprehensive computational study is conducted that compares the numerical tractability of various models across a suite of literature benchmark instances and a wide range of uncertainty sets. This study also provides, for the first time in the open literature, robust optimal solutions to process scheduling instances that involve uncertainty in production yields. © 2018 American Institute of Chemical Engineers AIChE J, 64: 3055–3070, 2018

Expanding scope and computational challenges in process scheduling

In this paper, we present a brief overview of enterprise-wide optimization and challenges in multiscale temporal modeling and integration of different models for the levels of planning, scheduling and control. Next, we review Generalized Disjunctive Programming (GDP), as a new modeling paradigm for scheduling problems that are illustrated with the STN and RTN models. We then address scheduling problems that expand the scope of the area: simultaneous scheduling and heat integration, pipeline scheduling, crude oil and refined products blending, and demand side management. We illustrate the advantage of the GDP modeling framework, describe effective strategies for global optimization, and describe multistage affinely adjustable robust optimization for uncertain interruptible load. We address integration of planning and scheduling, for which several approaches are reviewed, including use of traveling salesman constraints for multiperiod refinery planning, and multisite planning and scheduling of multiproduct batch plants. We report computational results to highlight the challenges.

Introducing the Concept of Roving Pumps in the Synthesis of Multipurpose Batch Plants

Presented in this manuscript is a novel technique for the exploitation of a unique feature of multipurpose batch plants, so called roving pumps. The discretized nature of batch processes allows for the allocation of pumps to different units and tasks depending on pump availability over the time horizon of interest. A MILP formulation is proposed to determine the optimal number and allocation of pumps within the batch plant while simultaneously synthesizing the plant and optimizing the production schedule with variable material transfer times. It is shown by way of an example that it is possible to decrease the number of pumps required for operation from seven to two pumps, while another example demonstrates a decrease in the number of pumps required from eleven to three. It is, therefore, possible to reduce capital and maintenance costs by implementing the formulation while still achieving or improving on existing throughputs.

An improved L-shaped method for two-stage convex 0–1 mixed integer nonlinear stochastic programs

In this paper, we propose an improved L-shaped method to solve large-scale two-stage convex 0–1 mixed-integer nonlinear stochastic programs with mixed-integer variables in both first and second stage decisions and with relatively complete recourse. To address the difficulties in solving large problems, we propose a Benders-like decomposition algorithm that includes both (strengthened) Benders cuts and Lagrangean cuts in the Benders master problem. The proposed algorithm is applied to solve a batch plant design problem under demand uncertainty, and a planning problem under demand and price uncertainty. It is shown that the proposed algorithm outperforms the commercial solvers, DICOPT, SBB, Alpha-ECP, and BARON, for the problems with a large number of scenarios. Also, although the proposed algorithm cannot close the duality gap, it is proved that it can yield a lower bound that is at least as tight as the one from Lagrangean decomposition.

Effect of Adjusting for Particle‐Size Distribution of Cement on Strength Development of Concrete

The massive construction projects such as nuclear power plants and gas storage plants operate their own batch plants for appropriate supply of a massive amount of ASTM Type I cement. The batch plants have difficulty responding to diversifying consumer requirements such as early strength development of concrete under the current limited production line of cement. In order to respond to the needs, this study collected several sizes of cement particles from different filters at a cement plant, and conducted the adjustment of cement particle‐size distribution to enhance the properties of concrete based on ASTM Type I cement. This paper shows the chemical properties and physical tests such as setting time and compression tests considering the effects of the distribution of cement particles. Also, the classification based on cement particle‐size distribution was defined as FMC (fineness modulus of cement). As FMC increased, compressive strength was relatively low at early age but the difference became smaller for later age. The test results show the effects of the adjusted mix proportion considering particle‐size distribution of cement on strength development of concrete. Therefore, it is possible to see that customized cement or concrete can be manufactured in response to various consumer requirements by introducing such method that by‐passes and collects fine or coarse cement in the cement‐crushing process and then re‐mixes them with OPC ASTM Type I cement depending on FMC.

Short-Term Scheduling of Batch Plants: Reformulation for Handling Material Transfer at the Same Event

This work presents a new variant of a unit-specific event-based model for short-term scheduling of batch plants. The motivation behind the proposed model is to further reduce number of events necessary to efficiently solve a given scheduling model. The main novelty in the proposed formulation is that the production and consumption tasks of each intermediate state are modeled to take place at the same event, in contrast to existing batch scheduling models where the consumption tasks normally start at the next event compared to the production tasks. This is the first time such a feature has been attempted in batch process scheduling, although it has been done in scheduling of continuous plants. Also, this is the first attempt where we are able to model the unit-specific events in their true sense, where the first event in each unit actually takes up the first event. The model is primarily applicable and useful for problems involving no recycle streams, although a hybrid approach has been suggested for probl...

Mixed-integer programming models for simultaneous batching and scheduling in multipurpose batch plants

We propose two novel discrete-time mixed-integer programming models for simultaneous batching and scheduling in multipurpose batch plants with storage constraints. The proposed models adopt two different modeling approaches. The first is based on explicit labeling of batches, while the second is based on identifying possible batch size intervals for each order and the corresponding unit routings. We also present extensions that allow us to consider limited shared utilities (with both fixed and time-varying availability and cost), storage with capacity limits and stage-dependent batch sizes. Finally, we study how instance characteristics (e.g. expected number of batches per order, uniformity in unit capacities) impact the effectiveness of the proposed models. We show that by carefully selecting the model allows us to effectively solve large-scale instances.

Statistical model for predicting and improving ready mixed concrete batch plants’ performance ratio under different influences

Producers of ready mixed concrete think of improving concrete batch plants’ performance ratio to provide the construction market with high quality standards, save cost and time. Ready mixed concrete (RMC) is a mixture of different materials, which is delivered to customer in unhardened state and freshly mixed. Major objective of this study was to provide a clear understanding for predicting and improving the concrete batch plants’ performance ratio by use analysis of large collected data from more and different concrete batch plants and determining the most effective factors that have a great effect on concrete batch plants’ performance ratio. Predicting the actual future performance ratio and production rates for any concrete batch plant according to a groups of effective factors is the essential sector which is suggested in this paper using smart modeling analysis. Improving the performance ratio of concrete batch plant is selected because of its importance in construction field by studying and analyzing the most effective factors. The study will be done by collecting and studying large detailed data through start of 2012 till the end of 2016 and it will illustrate the time, quantities, distances and factors which affect concrete batch plants’ performance ratio. This paper is divided into six main groups, which are illustrated as follows: (1) It concludes the historical information about concrete batch plants and RMC, and introduces the objective of this paper and the most convincing definitions for productivity and Performance Ratio (PR). (2) It explains general information for ready mixed concrete batch plants, classifies plant types for determining criteria of pumping method and declares the modeling techniques, concept of standard performance, measuring productivity difficulties, productivity cycles, factors affecting equipment productivity, applications of construction productivity, concrete placement process, concrete equipment selection and methods of construction. Factors affecting concrete batch plants’ performance ratio were mentioned and it discussed the related studies for problem statement. (3) It mentions the concrete batch plant classifications, advantages and disadvantages of ready mix concrete batch plants, mixing process and the methodology. It analyzes and improves the concrete batch plant performance ratio using optimizing of its most effective variables. (4) It introduces and analyzes all variables data at the same time using input phase by Statistics Package for Social Science (SPSS) software; then the estimated model was developed in the final form. Stepwise forms of the model were done using SPSS to reduce and simplify the proposed model for getting the alternative simplified formulas and neglect the variables that have lowest and negligible effect. On the other hand, correlation matrix was done to indicate what will be impacted on concrete batch plant performance ratio when each variable changes. (5) It introduces an application of general and simplified models for a real case study on Suez power station project, which uses ready mixed concrete with theoretical capacity equal to 160m3/h. The case study will be conducted to validate and verify the applications of the proposed general and simplified models to give optimum expressway for ready mixed concrete construction projects. (6) It introduces conclusions, recommendations and suggests future studies.

Discrete-time mixed-integer programming models for short-term scheduling in multipurpose environments

We present new discrete-time mixed-integer linear programming formulations for short-term scheduling in multi-purpose batch plants, the most general sequential production environment. We first discuss how multi-purpose batch plants can be expressed using State-Task Network and Resource-Task Network representations through batch-based definition of states (resources) and tasks. We then develop two models based on each representation that account for limited intermediate storage, and discuss extensions such as limited shared resources and time-varying resource availability/cost. Finally, we present several case studies to illustrate the applicability and performance of the proposed models.

Heat integration of multipurpose batch plants through multiple heat storage vessels

Energy minimisation in batch plants has garnered popularity over the past few decades, leading to direct and indirect heat integration techniques being formulated for multipurpose batch plants through the utilisation of mathematical formulations and insight-based methods Some mathematical formulations utilise predetermined scheduling frameworks which may result in suboptimal results, whilst other formulations only use one heat storage vessel which may cause limitations in the plant. The work presented in this manuscript is aimed at minimising energy consumption in multipurpose batch plants by exploring both direct and indirect heat integration through multiple heat storage vessels. It investigates the optimal number of heat storage vessels as well as design parameters, i.e. size and initial temperature of vessels. The cost of the heat storage vessels is considered within the model. The model is applied to two case studies resulting in significant increase in profits.

A computational framework for integrating campaign scheduling, dynamic optimization and optimal control in multi-unit batch processes

This contribution presents a framework for addressing the campaign scheduling, dynamic optimization and optimal control of batch processes in an integrated fashion. The strategy is comprised of an offline and an online phase. The first involves solving a conventional campaign scheduling problem and serves to generate key information needed in the second. The latter consists of a modified dynamic optimization/optimal control algorithm and serves to update the offline campaign schedule in real time as well as to provide the batch process with optimal control actions to achieve maximum campaign profit/performance. As a result of this two-phase architecture, the algorithm avoids the solution of a mixed-integer optimization problem online and can support virtually any process recipe structure including any type of recycle. To demonstrate its potential, we test the proposed methodology to solve the integrated campaign scheduling, dynamic optimization and optimal control of a batch plant for the production of nopol.